SOCIAL BIG DATA AND PRIVACY AWARENESS

Size: px
Start display at page:

Download "SOCIAL BIG DATA AND PRIVACY AWARENESS"

Transcription

1 SOCIAL BIG DATA AND PRIVACY AWARENESS Lin Sang January 26, 2015 Uppsala University Department of Informatics and Media Information Systems Master thesis, D level (30hp) Spring term 2014 Supervisor: Prof. Mats Edenius

2 Abstract Based on the rapid development of Big Data, the data from the online social network become a major part of it. Big data make the social networks became data-oriented rather than socialoriented. Taking this into account, this dissertation presents a qualitative study to research how does the data-oriented social network affect its users privacy management for nowadays. Within this dissertation, an overview of Big Data and privacy issues on the social network was presented as a background study. We adapted the communication privacy theory as a framework for further analysis how individuals manage their privacy on social networks. We study social networks as an entirety in this dissertation. We selected Facebook as a case study to present the connection between social network, Big Data and privacy issues. The data that supported the result of this dissertation collected by the face-to-face and in-depth interview study. As consequence, we found that the people divided the social networks into different level of openness in order to avoid the privacy invasions and violations, according to their privacy concern. They reduced and transferred their sharing from an open social network to a more close one. However, the risk of privacy problems actually raised because people neglected to understand the data process on social networks. They focused on managed the everyday sharing but too easily allowed other application accessed their personal data on the social network (such like the Facebook profile). Keywords: Big data, Online social networks, Communication privacy management theory 1

3 Acknowledgements There was significant experience for me to finish this dissertation. I would like to say thank you to my family and friends. They gave generous help and support to me, that inspired me a lot to get over the difficulty. Especially, I would express my grateful to my supervisor Prof. Mats Edenius. He lighted me up with his responsibility and professionalism. 2

4 Contents 1 Introduction Problem Overview Motivation Research questions Research Methodology Thesis Structure Background Big Data and data mining What is Big Data? What is data mining? Social Big Data and Privacy Leaking Social Big Data The Privacy Leaking Personal Data and Privacy Issues The New Thinking of Personal Data Identities The Increasing Risks of Personal Data Theoretical Framework 23 3

5 3.1 The Communication Privacy Management Theory The Privacy Management Theory for Social Networks Theory of Planned Behaviour Case Study: What can Facebook tell us? Facebook and Big Data Facebook and Privacy Issue Qualitative Study Participants Data Collection Data Analysis Empirical Finding Privacy, Privacy Setting and Privacy Policy Big Data and Trust on social networks Share Less, Share more carefully No Harm then No Care Conclusion and Discussion Answers of Research Questions Limitation Future Work A Interview Questions 43 4

6 List of Figures 1.1 The levels of research questions The research process (Oates, 2006) The knowledge structure The 3-V (Velocity, Variety and Volume) Model of Big Data The 4-V (Velocity, Variety, Volume and Veracity) Model of Big Data (IBM, 2014) The Model of Data Mining A Simple Model of Privacy Boundary The Model of Privacy Management Theory for social networks (Baatarjav, 2013) The Facebook Iceberg Model

7 Chapter 1 Introduction Chapter one provides an overview of this dissertation. It contains a total of five sections. In the following sections, it will describe the main problems that are going to be a focus within this dissertation. It also will describe in the main research questions and research methodology of this dissertation. At the end of this chapter, the structure of this dissertation is presented. 1.1 Problem Overview Now we are in the era of Big Data, and extracting useful information from Big Data become more and more important. With the continuously updating data mining techniques, information extracts from Big Data becomes much easier. On one hand, the combination of Big Data and data mining has a remarkable contribution in medicine, geographic information, biology, astronomy, etc.. It produces an excellent capability for processing complex information. Think about Siri (AI communication technology of Apple Company) and cloud computing, they only could stay in the imagination before, but now they come true and change our life. On the other hand, Big Data and data mining are gradually changing our understanding of privacy. The popularity of online social networks allowed people to relax their privacy control and made them more willing to share real information about themselves. The social network created digital file for each user, any operations from user were being recorded. In Facebook, for example, each personal digital file contained the data of user s profile, private message, wall posts, Like clicks, as well as the fully history of IP address (to record to movement of their users), videos and more (Patrick, 2013). The digital file of user on social networks originally used to analyze the user s interest and hobby, which to achieve the best advertising effectiveness. The social networks encouraged users to use real information and share everyday life. That creates huge data constantly imported to the social networks databases. 6

8 By using the over shared private information on social networks, social engineering attacks and identity theft in the past few years has particularly increased. The profile stolen and privacy invasions happened often on the social networks. Because of increasing online crimes, recently, the personal data which stored in the digital database of social networks begun to use as trustworthy evidence in legal cases in American (Patrick, 2013). The social networks were not going to keep the data for their own usage. Many third-party applications connected to the database of social networks and obtained the rights to access user s digital file, some of them also posted the advertisement on user s home page automatically (Baatarjav, 2013). Once the user did not choose to accept the request for the authorization of these third-party applications to use their data, they would not be able to use these applications. The privacy policy between the third-party applications and social network users was fully developed by the applications and social networks. Users did not allow to modify and amend the policy rules. According to the study by Rose, social network was the dangerous field for people to leak their personal data (Rose, 2011). Over-sharing and direct disclosure of everyday life sharing was the biggest problems for social networks users. People s communications on social networks could conditionally release their private information, but they still keep sharing without realized the danger about it (Rose, 2011). As an example of over-sharing on social networks, in 2010 a couple declared that they would not be at home with a given date on their social networks. Their friends robbed their house according to the post. In the same year, the Israel Defense Force cancelled their raid because one of their soldiers revealed their plan with location data on Facebook (Hodges and Creese, 2013). The communication privacy management theory by Petronius considered the problem of privacy leaking came from the private information co-owners (Petronio, 2002). Compared with the original information discloser (who shared the information first), some co-owners (who were audiences of the shared information) might have more power to control the information. Also, the co-owners cannot ensure that other co-owners who will not disclose the information again. Therefore, people should share information to someone and somewhere they trust, especially if the information was private. Another privacy management theory designed by Baatarjav considered people should select the information co-owner according to the assessment of social graph (Baatarjav, 2013). Based on the above theories, social networks made the privacy setting to help users to manage the audiences of their sharing. Facebook suggests that users should divide their friends into different lists, like family or colleague. The wall posts of Facebook recommended that public to the selected lists instead of to public totally. Google+ is similar to Facebook, which provided friend circle instead of friend lists. Twitter is even more simplify, its users only need to select to share as public to all users or to their own followers (Baatarjav, 2013). However, a recent study in 2013 found out that these privacy settings did not reduce the privacy leaking problem. They actually raised people s interest to share more but chat less (Cavusoglu et al., 2013). 7

9 1.2 Motivation Big data offer the convenience of technology, but at the same time it supports the social network looks into people s personal data. The discussion of information mining based on people s personal data becomes very sensitive nowadays. However, Information sharing is the nature of human behaviour. Social networks as the popular community for people to share information and communicate with each other, the efficient privacy management can improve the trust from social networks to its users. Except that, the development of Big Data determines the data analysis capabilities will be more automatic. Once the privacy information has become data, machine (or programme) can process data to understand us easier. Therefore, the thinking and attitude of social network users with privacy issues in the context of Big Data has important reference value. 1.3 Research questions Around to the points that we mentioned in the earlier sections, this dissertation is going to discuss the following research questions: RQ1: How does the Big Data affect individuals to manage their privacy boundary? RQ2: What is interviewee s attitude of the privacy security on social media? RQ3: What does the interviewee s saying and doing mean in terms of privacy matters? Figure 1.1: The levels of research questions. The research questions are designed into three levels (figure 1.1). The basic level is about the getting understanding the privacy boundary management of the social network users. This question relates to individual s share consciousness and behaviour on social networks. 8

10 The middle level of the research question is about the thinking of privacy security by social network users. The last level is related to the result of this research. This research hopes social network users can rethink about the hidden danger behind their own sharing behaviour on social networks. Their thinking has an important influence for the privacy issues of the future. 1.4 Research Methodology Designing a research methodology is trying to transform the research questions into a feasible study. The research in this dissertation is a social science study, since the aim of this research is going to study the consciousness (attitude of privacy leaking and Big Data matters) of social network users for nowadays. According to this, a qualitative methodology is adopted for this research. Based on the research model of Oates, a framework (research process) of this research presents in figure 1.2. Personal experience and motivation help themselves to come out with several possible research topics for doing a research. Literature review has two aims, to settle down a research topic which did not be fully addressed before, and refers evidence to support the viewpoint of the author. As the research topic of this dissertation need to have discussions based on different information, the literature review will go through the background chapter and case study. Also, the conceptual framework of research is provided by the literature study. Figure 1.2: The research process (Oates, 2006) A descriptive case study is the strategy of this research, which is to get a further and deeper understanding of the research topic. The analysis should tell a story, including a discussing of how people perceive what occurred, which in this case is the use of social networks (Oates, 2006). In this dissertation, Facebook is selected to be the main study object. The goal of this case study is to narrow the study focus from the background, and offer more concentrated questions through an interview study later. Thereby we can get a better understanding about user s experiences of social networks and what factors that might cause privacy issues for them. Data collection for this research is a semi-structured interview. The advantage of semi-structured 9

11 interview is that the researcher does not need to follow the original order of the questions list. Research can change questions or add more questions for a special issue during the interview. That helps inspire the interviewee to show their opinion under a proper conversation guide by researcher. The disadvantage is the researcher need to make clear their role of the interview, and try to grasp the topic firm instead of straying away. Since the data from interview are transformed from voice recording into documentation, the qualitative analysis is fit for this kind of data. To use the categories analysis, the slippery meaning of words will become the difficult point. Researchers need to put aside their own subjective view, to remain neutral and objective analysis of the data. As a tool, categories analysis and theme analysis is engaged into this research. 1.5 Thesis Structure In this section, the structure of this dissertation will be introduced here. There are total 7 chapters with a reference and an appendix section. Chapter 1 is a general overview of the whole dissertation. This research focuses on issues such as the principal problem overview, research questions and research methodology will be described in this chapter. Chapter 2 presents some theories and broad knowledge as the main background to support this dissertation s claim. It contains 3 main sections; each section has its own topic. The first section is going to introduce the definition of Big Data and data mining. In the next section, there will have a description of the relationship between the Social Big Data and the online privacy problems. The last section will study the identity problems and the increasing risks of personal data on social networks. Chapter 3 describes the main theoretical framework and extended theories for this dissertation. The communication privacy management (CPM) is focussed on studying the share behaviour between users of social networks and their friends. Another privacy management theory is focussed on creating a control pattern for the user to handle sharing on social networks. Both of them agree with what the content of daily sharing is the most difficult part for privacy control. Besides, the theories of planned behaviour (TPB) argue that people s subjectivity affect people s behaviour. External factor, such like Big Data also has an effect on people s awareness, which probably changes people s sharing on social networks. Chapter 4 is a case study of Facebook. Distinct from last chapter, this chapter is tending to borrow a real-life example (Facebook) to as a prelude to the later interview study of this dissertation. The purpose of this chapter is to analyse Facebook and its users share behaviour. At the same time, some common criticism of social networks and their users will be discussed. Chapter 5 comes out a design of an interview study. Based on the discussion and knowledge presented from the earlier chapter, requirement of the interviewee and the method of data collection and analysis are described in detail here. 10

12 Chapter 6 is responsible for presenting the empirical finding which is the data analysis result from the interview study. In this chapter, we presented four different findings can be described how people thinking their privacy management on social networks nowadays. And their opinion would present in the original words in order to keep their semantic unmodified. Chapter 7 as the final chapter, research questions will be answered. We would discuss our finding of how Big Data affects individuals change their privacy management on social networks. Meanwhile, the limitations and shortcomings of the study of this dissertation are also being included in this chapter. In the end, the direction and scope of future work will be given. 11

13 Chapter 2 Background In this chapter, some theory and general knowledge as the principal background of the study of this dissertation is presented (the knowledge structure in figure 2.1). Literature research supports the construction of this chapter. Figure 2.1: The knowledge structure 12

14 2.1 Big Data and data mining What is Big Data? In general, Big Data is not a certain term or rules. It is the generalized concept of a collection of relative data sets. At different specific time and environment, Big Data have different faces: it could be hundreds or thousands of hand writing records by an astrophysicist; it also could be the whole data on the google search engine server. But nowadays, the concept of Big Data is more tending to represent the virtual data which is in warehouses on computers and servers. Before 1997, Big Data has been mentioned as information explosion (Press, 2013). In 1997, Cox and Ellsworth first time information explosion named after Big Data. One year later, Weiss and Indurkya in their book Predictive data mining described Big Data is:... Very large collections of data - millions or even hundreds of millions of individual records... (Weiss and Indurkhya, 1998). Interestingly, the relationship between Big Data and data mining is already inseparable in this book. People considered Big Data provide a strong foundation for data mining. Even so, the definition of Big Data was still broadly at that time. More precise definition of Big Data was shown in Doug Laney of META group (now Gartner) came up with a 3-V model of Big Data (figure 2.2). This model is still in use. It defines three dimensions of Big Data in a 3D data management. Volume, variety and velocity are being identified as the main dimensions of Big Data (Laney, 2001). Figure 2.2: The 3-V (Velocity, Variety and Volume) Model of Big Data. 13

15 In this model, volume of data is the most intuitive dimension. This dimension means that Big Data accepted different forms of data at the same time. The rapid growth of data volume is reflected into two points. One is affected by the impact of the popularity of mobile devices and social networks. Another point is that there are multiple copies of the same data. People are more and more used to back-up their working documentation, both on their phone and personal computer. At the same time, data is growing at an unprecedented speed for generation and dissemination. This leads an increasingly demanding for processing, real-time data. Both transmission and processing of data are faced with the challenge of velocity upgrade (data velocity of the 4-v model). This is particularly evident on the social networks. Imagine that more than one million users submit a new post on Facebook at the same time, if the real-time data processing is slow and cannot handle such a lot data in a very short time, and then you are likely to have to wait a very long time to see other people s post. Because of this, in this dimension, the technology development of streaming data analysis becomes the key. Figure 2.3: The 4-V (Velocity, Variety, Volume and Veracity) Model of Big Data (IBM, 2014). However, the most important reason that makes data become big (Sagiroglu and Sinanc, 2013) is the variety of data. Theoretically, in a warehouse, structured data not only saves space saving, it also easy to search and processing. Indeed, now a large number of data (in particular the data stored on the network) are such unstructured. That leads data appear more random. Thus the search and processing of this kind of data greatly enhance the difficulty. 14

16 Semi-Structured data are a special kind of structured data; it uses tags or markers to separate data instead of putting it into a table or a real database (Sagiroglu and Sinanc, 2013). Compared with putting personal data into a table, writing a short personal introduction sentence can be considered as semi-structured data. Information will be extracted by processing. In 2014, IBM comes out a new 4-v model of Big Data (figure 2.3). The 4-v model based on the classic 3-v model: the definitions of data on velocity and data volume of the 4-v model follow the same definitions of the classic 3-v model. The 4-v model adds additional feature data veracity, which points out the common data quality problem of Big Data. Depending on the researches by IBM, the inaccurate data affect business decision. One of the three business leaders doesn t trust the data they used for making the decision (IBM, 2014). Also, poor data quality might lead to more money waste for organization and even up to the level of countries. Therefore, the new 4-v Big Data model is more appropriate to update identify the Big Data What is data mining? Accompanied by more widespread application of Big Data, data mining is also more and more in the spotlight. What is data mining? Data mining is part of the knowledge discovery (figure 2.4). Its purpose is to identify potential data relationships in vast amounts of data and presenting them as new information. Data mining helps people to know and understand the data in an effective way. There is the real world example of the application of data mining. Based on data mining, any different kind of data can be used to identify the person who created it (Patrick, 2013). Yves- Alexandre de Montjoye and César A. Hidalgo from MIT (Massachusetts Institute of Technology), they found out that whether data is anonymous or not, for a mobile device with four different location data is sufficient to pinpoint a user of the device. Still related to the location data. Adam Sadilek (researcher of Rochester University) and John Krumn (engineer from Microsoft s research lab) create a system that can predict people s approximate location by collecting and analysing their GPS data. For this system, they create a database to read the GPS data from 307 people and 396 vehicles in days. Out of surprise, the precision of this system is up to 80 percent, available for maximum in 80 weeks (Patrick, 2013). Coming back to the root of data mining, it blends three main field technologies together: there are classical statistics, artificial intelligence, and database research (Chen et al., 1996). These technologies are introduced a short history of data mining. People are very dependent on the database in the early days. They considered that data can be fully structured. In that period, the statistics play an important role in data mining. Statistic as the algorithms foundation of data mining is the efficient application of mathematical models. Excellent mathematical model allows the analysis of information becomes much easier. Later, different forms of data were born from the big bang of information, semi-structured and unstructured data are increasing in a short time. Traditional database cannot satisfy the storage requirements, at that time, the data warehouse came out in proper time and conditions. To be 15

17 Figure 2.4: The Model of Data Mining. the data foundation for data mining, database and data warehouse provides the basic functions of data collection, management and storage. Therefore, extract information and automatic learning from the mass of information become a new field of science. Among them, the most complex and broad concept is AI (artificial intelligence), it contains machine learning. AI allows a computer simulates the operation mode of the human brain. These modes of operation will be written as algorithms and machine learning is to let the computer automatically learn these algorithms, which help computers become more intelligent. Today, data mining algorithm is often also containing the characteristics of all these three technologies. This allows it to adapt to the complex situation of different data, such as Big Data. An example to use data mining technology on social networks is a machine learning approach called privacy wizard, which is proposed by Fang and LeFevre. This approach can use to find out similar users by matching users privacy setting. This approach already passed a test based on 45 users of Facebook, and the accuracy of results reaches 90 percent (Fang and LeFevre, 2010). 16

18 2.2 Social Big Data and Privacy Leaking Social Big Data Paper files in the past were the main way for people to get information. Electronic documents, databases and the Internet have changed the way for people to reach information. Online search engines also allow people to significantly reduce the time when they look for information (Kernighan, 2011). In the past, in the United States, it was not that easy can get other people s personal information. People are needed to visit government offices to access the public records. And they need to improve themselves by their personal identity before they can begin to read the public records. The public records did not allow to copy or transfer to other organizations easily. Nowadays, people are willing to share their real information on social networks. The social networks created a digital file for each user, any operations from user were being recorded. This kind of digital data from different social networks was called the Social Big Data (it means the Big Data which produced from social networks). In Facebook, for example, each personal digital file contains user s sent messages, photos, the Like information, as well as their full history of IP address, videos and more (Patrick, 2013). Instead of visiting government offices to access information about other people, the Facebook database is more convenient. Social networks as the most representative example of Big Data, which is worthwhile to ruminate over how they analyze their users. A report by BI Intelligence (Smith, 2014) pointed out that the social networks are trying to do the deep learning (kind of data mining) of their users. Within this report, they study the information collecting system of the most common social networks. Based on the operating characteristics of each social network is not the same. They have their own data collection focus. Such as the like function of Facebook. It is useful for getting to know the interest of users. Facebook will analyze the like history of users that in order to show the relevant advertisement on the user s home page. Facebook also analyse the user s text sharing and links sharing from other web sites, it extracts keywords from users sharing in order to match the possible advertisement content. A little bit different with Facebook, Twitter users is more likely to get attention on on-time news (or topics). For each news or topic, the number of followers means a certain degree of public tendencies. For example, whether for a TV program is popular or not, check the data on Twitter is good enough. LinkedIn is similar to Facebook and Twitter, it also collected huge personal data from its users, but it is used to create a social relationship graph with people s job or personal skills. The talent warehouse was built by LinkedIn to offer an efficient marketing on human resource. As a comparison, YouTube is very simple: it tried its best to recommend video for users according to their view history. 17

19 From above, it is not difficult to see, no matter what kind of social network is, the amount of data social networks are collecting is increasing according by their users needs. A large number of semi-structured and unstructured data require higher and higher investment cost of data storage. Data mining provides social networks a new motivation for accepting Big Data. At the same time, study the user s habits is the most direct way to improve the satisfaction for users. From this point of view, analysing users data are inevitable The Privacy Leaking To analyze users data can be used to provide better customer service, and create more business opportunities. Look at the advertising systems on mobile devices, which provide information about the nearby shops through the data from the global positioning system. Online shopping sites also recommended productions by analysis user s cookies and search history. There was news about Target (the American department store company) that even earlier than its female user to find out she was pregnant. By analysis the consumption custom of users. While other companies tried to integrate with the government databases, they provided a lot convenience for people peeping information on each other. To connect the election donation database of federal election commission, the website viewers can know the candidates, and which donations they received from friends and dignitaries. Included details of the candidate s name, occupation, address and even street number will be listed on the online map. Similarly, some companies were in advance to know their applicant by searching their name on social networks and search engine. However, not all the people want to be tracked and recorded all the time. Janet Vertesi as a pregnant woman and an assistant professor of sociology, she designed to hide her pregnant information from the Big Data (Goldstein, 2014). The process is very difficult and inconvenient, too much information needs to be kept as secret. Janet required her friends and family didn t mention her pregnancy on Facebook. People did follow her mind to not post anything about her on Facebook, but they still chatted with Janet or other people to talk about her pregnancy. Because of this, her pregnancy information still got into the Facebook s Big Data. Normal users don t realize that Facebook will track anything from them, not only the post on the home page but also the chat messages. There was a short history showed that people did keep changing their attitude to the online privacy. In 1998, a remarkable data from a survey by Harris Interactive/CITA showed that 80 percent of people were worried about the privacy of online shopping. At that time, social networks were a new idea of the internet. Later, people became over trust and depended on social network for communication (Rose, 2011). Only ten years, people were slowly overlooked the privacy problems. In 2008, a survey made by the Harris poll (a part of Harris Interactive) showed only 41 percent of young cared about online privacy. A later report was showed a new direction of people s attitude about the online privacy problems. In 2013, McCann did a global study of what people concerning was, there is 75 percent of people who joined the study paid attention to online privacy (McCannTruthCentral, 2013, Pingitore and Cavallaro, 2013). 18

20 The global financial crisis problems are the most popular concerning problem with people just got very little more attention than the online privacy problem. McCann also mentioned that the internet users agreed with that people were over sharing their personal data online. The social networks became the one of the reasons leaded the increase online sharing (McCannTruthCentral, 2013). Once the users want to delete their accounts, they don t have rights to require the social networks to completely delete their data. Despite all social networks have claimed that they wrote the Privacy Policy very clearly, the user s understanding of these terms is still quite vague. Privacy Policy is to protect the social networks can legitimate to use the user data, but the rules did not protect the user. Neglecting the safety of online privacy, in another way which is encourage more cybercrime. Social engineering attacks and identity theft in the past few years are particularly increasing. Social engineering attacks, this is a technology for stealing user important information which uses the network security vulnerabilities and users psychological neglect. A common trick is to send a request to update personal information and a link to a fake web page by , and steal users personal information via the fake web. Identity theft means the fraudulent use of other people s identity, to harm that people s the possessions or their personal rights. Although these cybercrimes are so appalling, social networks still overlooked the privacy problems of their users. Especially noteworthy is, on social networks, a lot of personal accounts are publicly available. For the economic drivers, social network companies will not encourage users to reduce their personal exposure data. They are keen to encourage users to upload their information to the web server. Users data become their new gold mine. 2.3 Personal Data and Privacy Issues The New Thinking of Personal Data Identities How do our online identities relate to our physical identities? An individual project calls SuperIdentity is trying to figure out this question (Hodges et al., 2012). The experts from computer science, law, psychology, anthropology, cyber and other disciplines are working together within this project. The purpose of this project is to trace out a set of factors which can use for identity authentication both for online and physical. Hodges and Creese were working on the personal identity problem with the SuperIdentity project. In their newly report, they described an identity model of personal data by biometrics, biographical, cyber and psychology (Hodges and Creese, 2013). 1. Biometrics (e.g. fingerprints, hand geometry, face, etc.) 2. Biographical (e.g. birthday, real name, home address, family, friends, etc.) 3. Cyber (e.g. user names, addresses, IP addresses, social sharing, etc.) 19

A Tracker Manual for High School Teachers

A Tracker Manual for High School Teachers A Tracker Manual for High School Teachers Thomas Peetz Bettina Berendt Department of Computer Science KU Leuven, Belgium firstname.lastname@cs.kuleuven.be November 19, 2012 This document describes the

More information

Is Web 2.0 Privacy Stuck in 1999, and Can They Do Better?

Is Web 2.0 Privacy Stuck in 1999, and Can They Do Better? Is Web 2.0 Privacy Stuck in 1999, and Can They Do Better? Jon Hyman and Kevin Bombino { jhyman, bombino } @ eecs.harvard.edu The Web 2.0 revolution is in full swing and entirely new classes of interactive

More information

Stay ahead of insiderthreats with predictive,intelligent security

Stay ahead of insiderthreats with predictive,intelligent security Stay ahead of insiderthreats with predictive,intelligent security Sarah Cucuz sarah.cucuz@spyders.ca IBM Security White Paper Executive Summary Stay ahead of insider threats with predictive, intelligent

More information

Google: Trust, Choice, and Privacy

Google: Trust, Choice, and Privacy Google: Trust, Choice, and Privacy Gus Meuli, Caitlin Finn Trust is hard to earn, easy to loose, and nearly impossible to win back. 1 This statement seems to ring true in the constantly changing world

More information

Facebook and Social Networking Security

Facebook and Social Networking Security Facebook and Social Networking Security By Martin Felsky November 2009 Table of Contents Introduction... 1 What is Facebook?... 2 Privacy Settings... 5 Friends... 7 Applications... 8 Twitter... 9 Should

More information

BIG DATA FUNDAMENTALS

BIG DATA FUNDAMENTALS BIG DATA FUNDAMENTALS Timeframe Minimum of 30 hours Use the concepts of volume, velocity, variety, veracity and value to define big data Learning outcomes Critically evaluate the need for big data management

More information

Facebook Smart Card FB 121211_1800

Facebook Smart Card FB 121211_1800 Facebook Smart Card FB 121211_1800 Social Networks - Do s and Don ts Only establish and maintain connections with people you know and trust. Review your connections often. Assume that ANYONE can see any

More information

How to avoid Five Blind Spots in Internet Filtering

How to avoid Five Blind Spots in Internet Filtering How to avoid Five Blind Spots in Internet Filtering If you re like most managers today, you d like to know what your employees are up to on company time, especially when they re using company PCs and laptops.

More information

Risk Analysis in Skype Software Security

Risk Analysis in Skype Software Security Risk Analysis in Skype Software Security Afnan AlOmrani, Rasheed AlZahrani, Eyas ElQawasmeh Information System Department College of Computer and Information Sciences King Saud University Riyadh, Saudi

More information

CORRALLING THE WILD, WILD WEST OF SOCIAL MEDIA INTELLIGENCE

CORRALLING THE WILD, WILD WEST OF SOCIAL MEDIA INTELLIGENCE CORRALLING THE WILD, WILD WEST OF SOCIAL MEDIA INTELLIGENCE Michael Diederich, Microsoft CMG Research & Insights Introduction The rise of social media platforms like Facebook and Twitter has created new

More information

DEVELOPING A SOCIAL MEDIA STRATEGY

DEVELOPING A SOCIAL MEDIA STRATEGY DEVELOPING A SOCIAL MEDIA STRATEGY Creating a social media strategy for your business 2 April 2012 Version 1.0 Contents Contents 2 Introduction 3 Skill Level 3 Video Tutorials 3 Getting Started with Social

More information

Cookie Policy. Introduction About Cookies

Cookie Policy. Introduction About Cookies Introduction About Cookies Cookie Policy Most websites you visit will use in order to improve your user experience by enabling that website to remember you, either for the duration of your visit (using

More information

Is Privacy A Cloud Illusion? Five Hidden Facts You Need to Know

Is Privacy A Cloud Illusion? Five Hidden Facts You Need to Know Is Privacy A Cloud Illusion? Five Hidden Facts You Need to Know 2014 StoAmigo. The information contained within these marketing materials is strictly for illustrative purposes, and the figures herein are

More information

This document has been produced following a request from the Hft National Speak Out Group for help with staying safe when using the internet.

This document has been produced following a request from the Hft National Speak Out Group for help with staying safe when using the internet. This document has been produced following a request from the Hft National Speak Out Group for help with staying safe when using the internet. Hft Safeguarding Group commissioned a member of Hft National

More information

10 SMART MONEY FACTS YOU NEED TO KNOW ABOUT BUSINESS SECURITY

10 SMART MONEY FACTS YOU NEED TO KNOW ABOUT BUSINESS SECURITY 10 SMART MONEY FACTS YOU NEED TO KNOW ABOUT BUSINESS SECURITY In the age of connected business work follows your workforce. You now have to keep track of your company assets and employees around the clock.

More information

AVOIDING ONLINE THREATS CYBER SECURITY MYTHS, FACTS, TIPS. ftrsecure.com

AVOIDING ONLINE THREATS CYBER SECURITY MYTHS, FACTS, TIPS. ftrsecure.com AVOIDING ONLINE THREATS CYBER SECURITY MYTHS, FACTS, TIPS ftrsecure.com Can You Separate Myths From Facts? Many Internet myths still persist that could leave you vulnerable to internet crimes. Check out

More information

experts in your field Get the profile: Managing your online reputation A Progressive Recruitment career guide Managing your online reputation

experts in your field Get the profile: Managing your online reputation A Progressive Recruitment career guide Managing your online reputation experts in your field Get the profile: A Progressive Recruitment career guide Contents Introduction... 2 Why you need an online reputation... 3 Monitoring your online reputation... 3 How to protect your

More information

Privacy Policy/Your California Privacy Rights Last Updated: May 28, 2015 Introduction

Privacy Policy/Your California Privacy Rights Last Updated: May 28, 2015 Introduction Privacy Policy/Your California Privacy Rights Last Updated: May 28, 2015 Introduction Welcome! TripleFirrre, LLC, dba Just Seconds Apart knows that safeguarding your privacy is serious business. Your privacy

More information

Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome

Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome Richard Breakiron Senior Director, Cyber Solutions Rbreakiron@vion.com Office: 571-353-6127 / Cell: 803-443-8002

More information

The Business Case for Data Governance

The Business Case for Data Governance Contents of This White Paper Data Governance...1 Why Today s Solutions Fall Short...2 Use Cases...3 Reviewing Data Permissions... 3 Reviewing Data Permissions with Varonis... 3 Reviewing User and Group

More information

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A SURVEY ON BIG DATA ISSUES AMRINDER KAUR Assistant Professor, Department of Computer

More information

Buyer s Guide to Big Data Integration

Buyer s Guide to Big Data Integration SEPTEMBER 2013 Buyer s Guide to Big Data Integration Sponsored by Contents Introduction 1 Challenges of Big Data Integration: New and Old 1 What You Need for Big Data Integration 3 Preferred Technology

More information

Market Research with Social Media

Market Research with Social Media Community Ebook / September 2012 / / 1 888 6radian Market Research with Social Media Gives you the direction and tools you need to use social media to become a more agile, engaged, profitable and productive

More information

(A trading company s staff, Ken, is opening the office s door)

(A trading company s staff, Ken, is opening the office s door) Information Security Animations Cloud Service Case Study (Script) First Set Setting: At a Trading Company (A trading company s staff, Ken, is opening the office s door) Ken: Boss, what happened? Mr. Chung:

More information

SEO search engine optimization

SEO search engine optimization How do I get visitors to my site? (the truth about SEO) SEO search engine optimization SEO is all about getting your site to the top of the list when people search for your topic in the major search engines:

More information

USES OF INTERNET TECHNOLOGIES IN CHILD SEXUAL ABUSE CASES. Peer to Peer Networking TYPES OF TECHNOLOGY. Presentation Supplement. How can it be used?

USES OF INTERNET TECHNOLOGIES IN CHILD SEXUAL ABUSE CASES. Peer to Peer Networking TYPES OF TECHNOLOGY. Presentation Supplement. How can it be used? TYPES OF TECHNOLOGY Peer to Peer Networking Networks in which computers are equal partners using common file sharing programs that allow users to connect directly to each other s computer hard drive to

More information

1. Layout and Navigation

1. Layout and Navigation Success online whether measured in visits, ad revenue or ecommerce transactions requires compelling content and intuitive design. It all starts with the fundamentals: the key building blocks to create

More information

The Big Data Paradigm Shift. Insight Through Automation

The Big Data Paradigm Shift. Insight Through Automation The Big Data Paradigm Shift Insight Through Automation Agenda The Problem Emcien s Solution: Algorithms solve data related business problems How Does the Technology Work? Case Studies 2013 Emcien, Inc.

More information

Reference Check: Is Your Boss Watching?

Reference Check: Is Your Boss Watching? & Reference Check: Is Your Boss Watching? Privacy and Your Facebook Profile www.ipc.on.ca Reference Check: Is Your Boss Watching? Privacy and Your Facebook Profile Facebook and other online social networks

More information

The Revolution of Retail Enterprise Network Marketing in Big Data Era

The Revolution of Retail Enterprise Network Marketing in Big Data Era The Revolution of Retail Enterprise Network Marketing in Big Data Era WANG Dan 1, LIU Teng 2 1. The School of Business, Beijing Wuzi University, 101149 2. The Graduate School, Beijing Wuzi University,

More information

CSIS Security Research and Intelligence Research paper: Threats when using Online Social Networks - 5 month later Date: 19 th October 2007

CSIS Security Research and Intelligence Research paper: Threats when using Online Social Networks - 5 month later Date: 19 th October 2007 CSIS Security Research and Intelligence Research paper: Threats when using Online Social Networks - 5 month later Date: 19 th October 2007 Written by Dennis Rand rand@csis.dk http://www.csis.dk Table of

More information

Marketing Online SEO Facebook Google Twitter YouTube

Marketing Online SEO Facebook Google Twitter YouTube Marketing Online SEO Facebook Google Twitter YouTube What is Internet Marketing? Internet marketing is considered to be broad in scope[1] because it not only refers to marketing on the Internet, but also

More information

Database and Data Mining Security

Database and Data Mining Security Database and Data Mining Security 1 Threats/Protections to the System 1. External procedures security clearance of personnel password protection controlling application programs Audit 2. Physical environment

More information

Cloud Computing: The Gathering Storm

Cloud Computing: The Gathering Storm Cloud Computing: Independent research Martin Wootton, RS Consulting Cloud Computing: The Gathering Storm What UK consumers really feel about cloud-based services We rely more than ever on computing and

More information

*Big Risks. Vast stores of information can provide organizations endless insight on their business. Managing and safeguarding all that data is

*Big Risks. Vast stores of information can provide organizations endless insight on their business. Managing and safeguarding all that data is *Big Risks. Vast stores of information can provide organizations endless insight on their business. Managing and safeguarding all that data is another story. S tories are emerging from the campaign trail

More information

Security Benefits of Cloud Computing

Security Benefits of Cloud Computing Security Benefits of Cloud Computing FELICIAN ALECU Economy Informatics Department Academy of Economic Studies Bucharest ROMANIA e-mail: alecu.felician@ie.ase.ro Abstract: The nature of the Internet is

More information

COPPA. How COPPA & Parental Intelligence Systems Help Parents Protect Their Kids Online. The Children s Online Privacy Protection Act

COPPA. How COPPA & Parental Intelligence Systems Help Parents Protect Their Kids Online. The Children s Online Privacy Protection Act The Children s Online Privacy Protection Act COPPA How COPPA & Parental Intelligence Systems Help Parents Protect Their Kids Online A uknow White Paper by Tim Woda, co founder of uknow.com, Inc Overview

More information

Keywords Big Data; OODBMS; RDBMS; hadoop; EDM; learning analytics, data abundance.

Keywords Big Data; OODBMS; RDBMS; hadoop; EDM; learning analytics, data abundance. Volume 4, Issue 11, November 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analytics

More information

Big Data a threat or a chance?

Big Data a threat or a chance? Big Data a threat or a chance? Helwig Hauser University of Bergen, Dept. of Informatics Big Data What is Big Data? well, lots of data, right? we come back to this in a moment. certainly, a buzz-word but

More information

International Journal of Advanced Engineering Research and Applications (IJAERA) ISSN: 2454-2377 Vol. 1, Issue 6, October 2015. Big Data and Hadoop

International Journal of Advanced Engineering Research and Applications (IJAERA) ISSN: 2454-2377 Vol. 1, Issue 6, October 2015. Big Data and Hadoop ISSN: 2454-2377, October 2015 Big Data and Hadoop Simmi Bagga 1 Satinder Kaur 2 1 Assistant Professor, Sant Hira Dass Kanya MahaVidyalaya, Kala Sanghian, Distt Kpt. INDIA E-mail: simmibagga12@gmail.com

More information

Student diaries: using technology to produce alternative forms of feedback

Student diaries: using technology to produce alternative forms of feedback Student diaries: using technology to produce alternative forms of feedback NUZ QUADRI University of Hertfordshire PETER BULLEN University of Hertfordshire AMANDA JEFFERIES University of Hertfordshire 214

More information

CSIS Security Research and Intelligence Research paper: Threats when using Online Social Networks Date: 16/05-2007

CSIS Security Research and Intelligence Research paper: Threats when using Online Social Networks Date: 16/05-2007 CSIS Security Research and Intelligence Research paper: Threats when using Online Social Networks Date: 16/05-2007 Written by Dennis Rand rand@csis.dk http://www.csis.dk Table of contents Table of contents...

More information

Website Privacy Policy Statement

Website Privacy Policy Statement Website Privacy Policy Statement This website ( CRSF Website ) is operated by Cal Ripken, Sr. Foundation, Inc. ( Company ) and this policy applies to all websites owned, operated, controlled and otherwise

More information

Profound Outdoors Privacy Policy

Profound Outdoors Privacy Policy Profound Outdoors Privacy Policy Our Commitment to Privacy Our Privacy Policy has been developed as an extension of our commitment to combine quality products and services with integrity in dealing with

More information

Big Data Introduction, Importance and Current Perspective of Challenges

Big Data Introduction, Importance and Current Perspective of Challenges International Journal of Advances in Engineering Science and Technology 221 Available online at www.ijaestonline.com ISSN: 2319-1120 Big Data Introduction, Importance and Current Perspective of Challenges

More information

MLg. Big Data and Its Implication to Research Methodologies and Funding. Cornelia Caragea TARDIS 2014. November 7, 2014. Machine Learning Group

MLg. Big Data and Its Implication to Research Methodologies and Funding. Cornelia Caragea TARDIS 2014. November 7, 2014. Machine Learning Group Big Data and Its Implication to Research Methodologies and Funding Cornelia Caragea TARDIS 2014 November 7, 2014 UNT Computer Science and Engineering Data Everywhere Lots of data is being collected and

More information

A Primer in Internet Audience Measurement

A Primer in Internet Audience Measurement A Primer in Internet Audience Measurement By Bruce Jeffries-Fox Introduction There is a growing trend toward people using the Internet to get their news and to investigate particular issues and organizations.

More information

Keep Yourself Safe from the Prying Eyes of Hackers and Snoopers!

Keep Yourself Safe from the Prying Eyes of Hackers and Snoopers! Protect Your Privacy Online P 7/1 Keep Yourself Safe from the Prying Eyes of Hackers and Snoopers! With the information in this article you can: Find out what secret information your PC is sharing with

More information

Cloud Computing Survey Perception of the companies. DPDP - Macedonia

Cloud Computing Survey Perception of the companies. DPDP - Macedonia Cloud Computing Survey Perception of the companies DPDP - Macedonia Survey regarding the awareness of the companies in relation to Cloud computing in Macedonia Executive summary The survey was conducted

More information

Overcoming Your Content Marketing Challenges

Overcoming Your Content Marketing Challenges Overcoming Your Content Marketing Challenges How to create great content your readers will share. 2012 Copyright Constant Contact, Inc. 13-3660 BEST PRACTICES GUIDE SOCIAL MEDIA MARKETING Engage your readers

More information

Chapter 11 Manage Computing Securely, Safely and Ethically. Discovering Computers 2012. Your Interactive Guide to the Digital World

Chapter 11 Manage Computing Securely, Safely and Ethically. Discovering Computers 2012. Your Interactive Guide to the Digital World Chapter 11 Manage Computing Securely, Safely and Ethically Discovering Computers 2012 Your Interactive Guide to the Digital World Objectives Overview Define the term, computer security risks, and briefly

More information

Just Net Coalition statement on Internet governance

Just Net Coalition statement on Internet governance Just Net Coalition statement on Internet governance (Just Net Coalition is a global coalition of civil society actors working on Internet governance issues) All states should work together to provide a

More information

Shannon Wilkinson Ask The Reputation Management Experts

Shannon Wilkinson Ask The Reputation Management Experts Shannon Wilkinson Ask The Reputation Management Experts By Zac Johnson October 8, 2013 at 11:00 am Shannon Wilkinson is an expert in the area of reputation management and public relations. Shannon knows

More information

Governance and Legal Issues for Port Commissioners

Governance and Legal Issues for Port Commissioners Governance and Legal Issues for Port Commissioners Thomas H. Tanaka Senior Port Counsel Port of Seattle June 3, 2014 AAPA Commissioners Seminar Seattle, WA First Principle Governing vs. Operating What

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of

More information

PR and Social Media. James L. Horton

PR and Social Media. James L. Horton PR and Social Media James L. Horton Newspapers are withering. Network television has watched audiences decline. Radio is splintered. Magazines are shrinking. Meanwhile, there are millions of bloggers and

More information

Why Your Job Search Isn t Working

Why Your Job Search Isn t Working Why Your Job Search Isn t Working 6 mistakes you re probably making and how to fix them I t s easy to think that your lack of success in finding a new job has nothing to do with you. After all, this is

More information

The Five Biggest MISSED Internet Marketing Opportunities Most Lawyers Don't Know About

The Five Biggest MISSED Internet Marketing Opportunities Most Lawyers Don't Know About The Five Biggest MISSED Internet Marketing Opportunities Most Lawyers Don't Know About Many lawyers and other professionals equate internet marketing with Search Engine Optimization (SEO). And while SEO

More information

GRAPHICAL USER INTERFACE, ACCESS, SEARCH AND REPORTING

GRAPHICAL USER INTERFACE, ACCESS, SEARCH AND REPORTING MEDIA MONITORING AND ANALYSIS GRAPHICAL USER INTERFACE, ACCESS, SEARCH AND REPORTING Searchers Reporting Delivery (Player Selection) DATA PROCESSING AND CONTENT REPOSITORY ADMINISTRATION AND MANAGEMENT

More information

Common Facebook issues

Common Facebook issues Common Facebook issues and how to resolve them Introduction Love it or loathe it, with over 28 million users in the UK alone, Facebook cannot be ignored. It is the social network of choice for many young

More information

Cookie Policy. Introduction About Cookies

Cookie Policy. Introduction About Cookies Introduction About Cookies Cookie Policy Most websites you visit will use in order to improve your user experience by enabling that website to remember you, either for the duration of your visit (using

More information

Why You Need Email Archiving

Why You Need Email Archiving Why You Need Email Archiving www.exclaimer.com Table of Contents Introduction...2 The IT Administrator...3 The Email User...5 The Team Leader...6 The Senior Manager/Business Owner...7 Conclusion...8-1

More information

Thank you for visiting this website, which is owned by Essendant Co.

Thank you for visiting this website, which is owned by Essendant Co. Essendant Online Privacy Policy Thank you for visiting this website, which is owned by Essendant Co. Please take a few minutes to review this Policy. It describes how we will collect, use, and share information

More information

Whitepaper on AuthShield Two Factor Authentication and Access integration with Microsoft outlook using any Mail Exchange Servers

Whitepaper on AuthShield Two Factor Authentication and Access integration with Microsoft outlook using any Mail Exchange Servers Whitepaper on AuthShield Two Factor Authentication and Access integration with Microsoft outlook using any Mail Exchange Servers By INNEFU Labs Pvt. Ltd Table of Contents 1. Overview... 3 2. Threats to

More information

A BUSINESS CASE FOR BEHAVIORAL ANALYTICS. White Paper

A BUSINESS CASE FOR BEHAVIORAL ANALYTICS. White Paper A BUSINESS CASE FOR BEHAVIORAL ANALYTICS White Paper Introduction What is Behavioral 1 In a world in which web applications and websites are becoming ever more diverse and complicated, running them effectively

More information

Profitable vs. Profit-Draining Local Business Websites

Profitable vs. Profit-Draining Local Business Websites By: Peter Slegg (01206) 433886 07919 921263 www.besmartmedia.com peter@besmartmedia.com Phone: 01206 433886 www.besmartmedia.com Page 1 What is the Difference Between a Profitable and a Profit-Draining

More information

Refog. Maxim Ananov, REFOG Help Desk

Refog. Maxim Ananov, REFOG Help Desk Refog Maxim Ananov, REFOG Help Desk 1. How does it work? Is it installed locally or does it work in the cloud? System administrator installs Refog Monitor on a computer that is to be used to. Installation

More information

Cyber Security. An Executive Imperative for Business Owners. 77 Westport Plaza, St. Louis, MO 63416 p 314.439.4700 f 314.439.4799

Cyber Security. An Executive Imperative for Business Owners. 77 Westport Plaza, St. Louis, MO 63416 p 314.439.4700 f 314.439.4799 Cyber Security An Executive Imperative for Business Owners SSE Network Services www.ssenetwork.com 77 Westport Plaza, St. Louis, MO 63416 p 314.439.4700 f 314.439.4799 Pretecht SM by SSE predicts and remedies

More information

Enabling Blind People to See Farther: Protection & Ease Information Platform. on Smartphones for the Blind

Enabling Blind People to See Farther: Protection & Ease Information Platform. on Smartphones for the Blind Enabling Blind People to See Farther: Protection & Ease Information Platform on Smartphones for the Blind Author: Steve Koon Founder, Innovate 99 Impact Investment Consulting Technology has changed our

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Exam Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) Once a company knows a consumer's preferences, the company can predict, without asking

More information

BIG DATA: BIG CHALLENGE FOR SOFTWARE TESTERS

BIG DATA: BIG CHALLENGE FOR SOFTWARE TESTERS BIG DATA: BIG CHALLENGE FOR SOFTWARE TESTERS Megha Joshi Assistant Professor, ASM s Institute of Computer Studies, Pune, India Abstract: Industry is struggling to handle voluminous, complex, unstructured

More information

Blogs, Facebook accounts, Twitter accounts and multiple e-mail accounts. Photos on Flickr, documents in Google Docs and videos on YouTube

Blogs, Facebook accounts, Twitter accounts and multiple e-mail accounts. Photos on Flickr, documents in Google Docs and videos on YouTube Estate Planning for Your Digital Assets By Dennis Kennedy March 2010 Given the wealth of information we have housed on our computers and the Internet today, smart estate and succession planning includes

More information

RDM on Demand Privacy Policy

RDM on Demand Privacy Policy RDM on Demand Privacy Policy Ataccama Corp. www.ataccama.com info@ataccama.com Toronto, CA Prague, CZ London, UK Stamford, US 1 ATACCAMA RDM ON DEMAND PRIVACY POLICY 1. Ataccama Corp. ("Ataccama") and

More information

GENERAL TERMS OF USE MYGEONAUTE.COM

GENERAL TERMS OF USE MYGEONAUTE.COM GENERAL TERMS OF USE MYGEONAUTE.COM 1. Foreword These conditions of use are concluded between the User of the website (hereinafter referred to as "User") and the "mygeonaute.com" site operated by the company

More information

The Convergence of IT Big Data & Marketing. Regis McKenna 2014 Alliance of Chief Executives

The Convergence of IT Big Data & Marketing. Regis McKenna 2014 Alliance of Chief Executives The Convergence of IT Big Data & Marketing Regis McKenna 2014 Alliance of Chief Executives 1 2 Technology Marketing 3 Major Technologies Driving Marketing Mass Production & Mass Media Mainframe computer

More information

IT Requirements for the Eyelation Kiosks

IT Requirements for the Eyelation Kiosks Internet access & power at the kiosk site IT Requirements for the Eyelation Kiosks We prefer a wired network port and standard 120v power outlet at the physical kiosk location for the best speed and reliability.

More information

Cloudy Privacy Computing

Cloudy Privacy Computing Cloudy Privacy Computing Rebecca Herold, CIPP, CISSP, CISA, CISM, FLMI Final Draft for December 2008 CSI Alert Is cloud computing cumulous or cirrus? At Thanksgiving dinner, some of my relatives (none

More information

Privacy Policy and Notice of Information Practices

Privacy Policy and Notice of Information Practices Privacy Policy and Notice of Information Practices Effective Date: April 27, 2015 BioMarin Pharmaceutical Inc. ("BioMarin") respects the privacy of visitors to its websites and online services and values

More information

Automatic measurement of Social Media Use

Automatic measurement of Social Media Use Automatic measurement of Social Media Use Iwan Timmer University of Twente P.O. Box 217, 7500AE Enschede The Netherlands i.r.timmer@student.utwente.nl ABSTRACT Today Social Media is not only used for personal

More information

SEO OVERVIEW. We want you educated about SEO before we start! By Mary Cary NewMediaLegalMarketing / Video Blog Marketing / Video Studio Sausalito

SEO OVERVIEW. We want you educated about SEO before we start! By Mary Cary NewMediaLegalMarketing / Video Blog Marketing / Video Studio Sausalito SEO OVERVIEW We want you educated about SEO before we start! By Mary Cary NewMediaLegalMarketing / Video Blog Marketing / Video Studio Sausalito (415) 690 7112 Table of Contents Knowledge is Power What

More information

e-safety for Parents

e-safety for Parents e-safety for Parents Helenswood Academy Published June 2014 1 Contents Introduction 4 The Web 5 Children online 6 Friends of your child 7 Information about your child 8 Ownership of your child s technology

More information

Improving Online Collaboration within the IFIP Working Group on Human Aspects of Information Security and Assurance

Improving Online Collaboration within the IFIP Working Group on Human Aspects of Information Security and Assurance Improving Online Collaboration within the IFIP Working Group on Human Aspects of Information Security and Assurance Abstract O. Burton and N. Clarke Centre for Security, Communications and Network Research,

More information

Adaptive Business Management Systems Privacy Policy

Adaptive Business Management Systems Privacy Policy Adaptive Business Management Systems Privacy Policy Updated policy: Effective on July 01, 2013 This privacy statement describes how Adaptive Business Management Systems collects and uses the personal information

More information

Leveraging Social Media

Leveraging Social Media Leveraging Social Media Social data mining and retargeting Online Marketing Strategies for Travel June 2, 2014 Session Agenda 1) Get to grips with social data mining and intelligently split your segments

More information

Cookie Policy. Introduction About Cookies

Cookie Policy. Introduction About Cookies Introduction About Cookies Cookie Policy Most websites you visit will use cookies in order to improve your user experience by enabling that website to remember you, either for the duration of your visit

More information

Managing Cloud Server with Big Data for Small, Medium Enterprises: Issues and Challenges

Managing Cloud Server with Big Data for Small, Medium Enterprises: Issues and Challenges Managing Cloud Server with Big Data for Small, Medium Enterprises: Issues and Challenges Prerita Gupta Research Scholar, DAV College, Chandigarh Dr. Harmunish Taneja Department of Computer Science and

More information

ABC PRIVACY POLICY. The ABC is strongly committed to protecting your privacy when you interact with us, our content, products and services.

ABC PRIVACY POLICY. The ABC is strongly committed to protecting your privacy when you interact with us, our content, products and services. ABC PRIVACY POLICY The ABC is strongly committed to protecting your privacy when you interact with us, our content, products and services. Our goal is to provide you and your family with media experiences

More information

Seven Things You Must Know Before Hiring a Divorce Lawyer

Seven Things You Must Know Before Hiring a Divorce Lawyer Seven Things You Must Know Before Hiring a Divorce Lawyer Introduction Divorce is a stressful time for everyone. Whether you ve been together for 3 months or 30 years, it s important that you follow through

More information

Integration of Learning Management Systems with Social Networking Platforms

Integration of Learning Management Systems with Social Networking Platforms Integration of Learning Management Systems with Social Networking Platforms E-learning in a Facebook supported environment Jernej Rožac 1, Matevž Pogačnik 2, Andrej Kos 3 Faculty of Electrical engineering

More information

QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM

QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM QlikView Technical Case Study Series Big Data June 2012 qlikview.com Introduction This QlikView technical case study focuses on the QlikView deployment

More information

So let me introduce you to Ian Brodie:

So let me introduce you to Ian Brodie: Interview Email Persuasion Ian Brodie Dec 2013 Petra Mayer: Hello and welcome to this Special Interview. My name is Petra Mayer, Lead Strategist at Petra Mayer Consulting Online Strategy Made Easy. As

More information

Trending with NextGen travelers. Understanding the NextGen consumer-traveler

Trending with NextGen travelers. Understanding the NextGen consumer-traveler Trending with NextGen travelers Understanding the NextGen consumer-traveler Why NextGen is important Amadeus research study: the NextGen traveler Understanding trendsetters and future heavy consumers of

More information

Ten Tips for Managing Risks on Convergent Networks The Risk Management Group

Ten Tips for Managing Risks on Convergent Networks The Risk Management Group Ten Tips for Managing Risks on Convergent Networks The Risk Management Group April 2012 Sponsored by: Lavastorm Analytics is a global business performance analytics company that enables companies to analyze,

More information

Multi-Factor Authentication

Multi-Factor Authentication Making the Most of Multi-Factor Authentication Introduction The news stories are commonplace: Hackers steal or break passwords and gain access to a company s data, often causing huge financial losses to

More information

National Cyber Security Month 2015: Daily Security Awareness Tips

National Cyber Security Month 2015: Daily Security Awareness Tips National Cyber Security Month 2015: Daily Security Awareness Tips October 1 New Threats Are Constantly Being Developed. Protect Your Home Computer and Personal Devices by Automatically Installing OS Updates.

More information