Shroudbase Technical Overview
|
|
- Jeffery McCormick
- 8 years ago
- Views:
Transcription
1 Shroudbase Technical Overview Differential Privacy Differential privacy is a rigorous mathematical definition of database privacy developed for the problem of privacy preserving data analysis. Specifically, it ensures that a computation does not reveal information about individual records present in the input by requiring that the computation behaves almost identically on any two databases which differ by at most a single record. Formally, a mechanism M mapping datasets to distributions over an output space R is -differentially private if for every S R and for all datasets A, A 0 for which the number of records which would have been added or removed to change A to A 0 is less than or equal to one, Pr[M(A) 2 S] apple e Pr[M(A 0 ) 2 S] We can interpret the definition as follows: If there are two databases, one with a presence of an individual s data, A, and one without this individual s data, A 0,thenforsmallvaluesof, thereisnooutput an adversary could use to distinguish between A and A 0.Assuch,itisvirtuallyimpossibletoidentifyany information about an individual when differential privacy is achieved. It ensures that personal information about an individual will not be disclosed by participating in a dataset regardless of any external information or datasets, regardless of the computational power of an adversary and regardless of any statistical techniques which exist or may be developed in the future. Differential privacy is typically achieved by adding statistical noise to the output of queries or, more abstractly, to the method of choosing responses to queries. A decade of research in the field has produced an array of algorithms which achieve differential privacy for a wide range of data analysis methods. These algorithms have been refined to introduce minimal noise, and come with strong, provable guarantees of accuracy. However, this interactive model requires that noise be drawn from a fixed distribution on multiple occasions, which introduces a critical drawback: the database comes with a budget and querying is costly once this budget is exhausted, differential privacy is no longer satisfied. Producing Synthetic Data The key to practical, differentially private data analysis is generating synthetic databases. These databases are computed by differentially private algorithms on the original data, and therefore ensure that any computation over the data is differentially private. As a result, these databases do not impose any limitations on data access, and remain private even in the event of a security breach. An example of a simple method for producing synthetic data on low-dimensional datasets which accurately answers statistical queries (queries which count the number of records which satisfy a certain predicate) is the MWEM algorithm. MWEM (Multiplicative Weights Exponential Mechanism) maintains an approximating dataset over a domain of records, initialized to be a uniform distribution over the set of records. At each iteration, the algorithm chooses a query with a high error on the approximate data, poses this query to the true data, and improves the approximate data to more accurately answer the specific query. After a specified number of iterations, the algorithm outputs the average of the approximate databases produced at each iteration as the 1
2 synthetic data. The accuracy of this algorithm, defined to be the maximum error of any query, is provably logarithmic in the number queries and asymptotically smaller than the number of records. The mathematical details are as follows: The algorithm takes as input a database D, anumberofrecordsn, asetq of queries, a number of iterations T,a privacy parameter (a small number). First, a distribution A 0 is initialized to be the uniform distribution over the universe of records. The exponential mechanism, which satisfies differential privacy is used to choose queries. At a given iteration i of the algorithm, the exponential mechanism chooses a query q i from the distribution: exp( q(a i 1 ) q(d) ) 2T where A i 1 is the approximate database at iteration i 1 and D is the true data. The mechanism for posing the query to the data achieves differential privacy by adding Laplace noise to the output of the query. That is, the measurement of the output of a query is taken to be: 2T m i = q i (D)+Lap At each iteration i, theapproximatedatabaseisupdatedusingthemultiplicativeweightsalgorithm: A i (x) =A i 1 (x) exp q i (x) (m i q i (A i 1 )) 2n Once the algorithm has completed T iterations, A = avg i<t A i is outputed as a synthetic database. The worst case error of the algorithm is given by: r log U 10T log Q max q2q q(a) q(d) apple2n + T The MWEM algorithm, however, is not a universal solution to the release of synthetic data. The algorithm has worst case exponential complexity, so it is not practical for high-dimensional datasets. More over, the accuracy guarantees it provides hold only for linear queries. Although compositions of linear queries can be used to implement a broad range of statistical techniques, MWEM does not provide any accuracy guarantees for certain crucial methods in data analysis, such as regressions. Shroudbase The approach used in this algorithm is the foundation for many of the advanced algorithms deployed by the Shroudbase platform, which produces and manages synthetic data through differentially private mechanisms. Shroudbase s patent-pending software deploys a repertoire of privacy preserving algorithms to enable accurate data analytics on sensitive data, far beyond the capabilities of MWEM. These range from producing summary statistics to machine learning and optimization. Shroudbase: efficiently produces synthetic data on terabytes of high-dimensional datasbases efficiently produces synthetic data to preserve accuracy of generalized linear models, such as regressions maintains these private databases in a centralized, easy-to-use platform answers millions of MySQL queries without requiring the user to specify them in advance Shroudbase is a platform for producing and managing these differentially private synthetic databases. 2
3 Shroudbase Infrastructure I. Privatization Privatizing data with Shroudbase is a one step process. The client simply enters the information required to access their database along with an endpoint to store the synthetic data. The platform currently privatizes any structured data, including MySQL, PostgreSQL, Microsoft SQL, sqlite3, Excel spreadsheets, and csv files. The privatization procedure can be run through our cloud cluster or locally by installing the Shroudbase Database Management System on the client s machines. If the client uses a local implementation, then the entire procedure can be executed without Shroudbase ever reading or storing any sensitive information. 3
4 II. Storage Privatized data is stored with the Shroudbase Cloud Database Service. While many online storage systems only protect data in transit, Shroudbase ensures that the only data that enters the cloud is synthetic data with no personally identifiable information. Practically speaking, this means that nobody a hacker, government agency, an employee of Shroudbase can ever access any personal information through Shroudbase, because it simply isn t there. Clients access this service through the Shroudbase administrative control panel or Shroudbase Database Management System, an installable package for controlled data access and administration. Clients also have the option of storing the privatized data locally. 4
5 III. Querying The Shroudbase Query Client provides an easy and intuitive way to use privatized databases. This client interface takes in SQL formatted commands and outputs responses in a format similar to MySQL s client interface. This can be run by calling sb from the commandline with the appropriate hostname and port for the database the user is connected to. Queries with Shroudbase are identical to MySQL queries, and Shroudbase supports most statistical functions found in MySQL. IV. Updating Shroudbase s patent-pending technology supports inserting additional data into the database while preserving privacy. When additional data is added, the Shroudbase system stores the data in an intermediary state until the Shroudbase server detects that an update needs to occur. When an update occurs, the privatization job is off-loaded to Shroudbase s privatization infrastructure to be recomputed in the cloud. Note: For clients who wish to run specialized analysis not currently supported by Shroudbase synthetic datasets, we provide custom implementations of adaptive differentially private mechanisms. 5
future proof data privacy
2809 Telegraph Avenue, Suite 206 Berkeley, California 94705 leapyear.io future proof data privacy Copyright 2015 LeapYear Technologies, Inc. All rights reserved. This document does not provide you with
More informationDifferential privacy in health care analytics and medical research An interactive tutorial
Differential privacy in health care analytics and medical research An interactive tutorial Speaker: Moritz Hardt Theory Group, IBM Almaden February 21, 2012 Overview 1. Releasing medical data: What could
More informationPrerequisites. Course Outline
MS-55040: Data Mining, Predictive Analytics with Microsoft Analysis Services and Excel PowerPivot Description This three-day instructor-led course will introduce the students to the concepts of data mining,
More informationSoftware Design Proposal Scientific Data Management System
Software Design Proposal Scientific Data Management System Alex Fremier Associate Professor University of Idaho College of Natural Resources Colby Blair Computer Science Undergraduate University of Idaho
More informationThis paper is directed to small business owners desiring to use. analytical algorithms in order to improve sales, reduce attrition rates raise
Patrick Duff Analytical Algorithm Whitepaper Introduction This paper is directed to small business owners desiring to use analytical algorithms in order to improve sales, reduce attrition rates raise profits
More informationHexaware E-book on Predictive Analytics
Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,
More informationDiploma Of Computing
Diploma Of Computing Course Outline Campus Intake CRICOS Course Duration Teaching Methods Assessment Course Structure Units Melbourne Burwood Campus / Jakarta Campus, Indonesia March, June, October 022638B
More informationRingStor User Manual. Version 2.1 Last Update on September 17th, 2015. RingStor, Inc. 197 Route 18 South, Ste 3000 East Brunswick, NJ 08816.
RingStor User Manual Version 2.1 Last Update on September 17th, 2015 RingStor, Inc. 197 Route 18 South, Ste 3000 East Brunswick, NJ 08816 Page 1 Table of Contents 1 Overview... 5 1.1 RingStor Data Protection...
More informationA Practical Application of Differential Privacy to Personalized Online Advertising
A Practical Application of Differential Privacy to Personalized Online Advertising Yehuda Lindell Eran Omri Department of Computer Science Bar-Ilan University, Israel. lindell@cs.biu.ac.il,omrier@gmail.com
More informationOpenText Actuate Big Data Analytics 5.2
OpenText Actuate Big Data Analytics 5.2 OpenText Actuate Big Data Analytics 5.2 introduces several improvements that make the product more useful, powerful and flexible for end users. A new data loading
More informationL3: Statistical Modeling with Hadoop
L3: Statistical Modeling with Hadoop Feng Li feng.li@cufe.edu.cn School of Statistics and Mathematics Central University of Finance and Economics Revision: December 10, 2014 Today we are going to learn...
More informationMachine Data Analytics with Sumo Logic
Machine Data Analytics with Sumo Logic A Sumo Logic White Paper Introduction Today, organizations generate more data in ten minutes than they did during the entire year in 2003. This exponential growth
More informationhmetrix Revolutionizing Healthcare Analytics with Vertica & Tableau
Powered by Vertica Solution Series in conjunction with: hmetrix Revolutionizing Healthcare Analytics with Vertica & Tableau The cost of healthcare in the US continues to escalate. Consumers, employers,
More informationConnect to MySQL or Microsoft SQL Server using R
Connect to MySQL or Microsoft SQL Server using R 1 Introduction Connecting to a MySQL database or Microsoft SQL Server from the R environment can be extremely useful. It allows a research direct access
More informationMcAfee Web Reporter Turning volumes of data into actionable intelligence
McAfee Web Reporter Turning volumes of data into actionable intelligence Business today is more Internet-dependent than ever before. From missioncritical services to productivity tools, Internet access
More informationDATA MINING TOOL FOR INTEGRATED COMPLAINT MANAGEMENT SYSTEM WEKA 3.6.7
DATA MINING TOOL FOR INTEGRATED COMPLAINT MANAGEMENT SYSTEM WEKA 3.6.7 UNDER THE GUIDANCE Dr. N.P. DHAVALE, DGM, INFINET Department SUBMITTED TO INSTITUTE FOR DEVELOPMENT AND RESEARCH IN BANKING TECHNOLOGY
More informationDesigning a Data Solution with Microsoft SQL Server 2014
20465C - Version: 1 22 June 2016 Designing a Data Solution with Microsoft SQL Server 2014 Designing a Data Solution with Microsoft SQL Server 2014 20465C - Version: 1 5 days Course Description: The focus
More informationTHE FIRST UNIFIED DATABASE SECURITY SOLUTION. Product Overview Security. Auditing. Caching. Masking.
THE FIRST UNIFIED DATABASE SECURITY SOLUTION Product Overview Security. Auditing. Caching. Masking. 2 The First Unified Database Security Solution About the products The GreenSQL family of Unified Database
More informationHTSQL is a comprehensive navigational query language for relational databases.
http://htsql.org/ HTSQL A Database Query Language HTSQL is a comprehensive navigational query language for relational databases. HTSQL is designed for data analysts and other accidental programmers who
More informationYOUR APP. OUR CLOUD.
YOUR APP. OUR CLOUD. The Original Mobile APP! Copyright cloudbase.io 2013 2 THE MARKET Mobile cloud market in billions of $ $ 16 $ 14 $ 12 $ 10 $14.5bn The size of the mobile cloud market in 2015 $ 8 $
More informationAdvanced analytics at your hands
2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previously
More informationPracticing Differential Privacy in Health Care: A Review
TRANSACTIONS ON DATA PRIVACY 5 (2013) 35 67 Practicing Differential Privacy in Health Care: A Review Fida K. Dankar*, and Khaled El Emam* * CHEO Research Institute, 401 Smyth Road, Ottawa, Ontario E mail
More informationPractical Data Science with Azure Machine Learning, SQL Data Mining, and R
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be
More informationInstallation Guide. Non Linear Services. August 2015. Delivering the Moment
Non Linear Services August 2015 Delivering the Moment Publication Information 2015 Imagine Communications Corp. Proprietary and Confidential. Imagine Communications considers this document and its contents
More informationUse Data to Advance Institutional Performance
Use Data to Advance Institutional Performance Published: September 2014 For the latest information, please see www.microsoft.com/education Facing Increasing Demands for Accountability... 1 Developing a
More informationTurning Data into Actionable Insights: Predictive Analytics with MATLAB WHITE PAPER
Turning Data into Actionable Insights: Predictive Analytics with MATLAB WHITE PAPER Introduction: Knowing Your Risk Financial professionals constantly make decisions that impact future outcomes in the
More informationSisense. Product Highlights. www.sisense.com
Sisense Product Highlights Introduction Sisense is a business intelligence solution that simplifies analytics for complex data by offering an end-to-end platform that lets users easily prepare and analyze
More informationBiDAl: Big Data Analyzer for Cluster Traces
BiDAl: Big Data Analyzer for Cluster Traces Alkida Balliu, Dennis Olivetti, Ozalp Babaoglu, Moreno Marzolla, Alina Sirbu Department of Computer Science and Engineering University of Bologna, Italy BigSys
More informationMIGRATING TO AVALANCHE 5.0 WITH MS SQL SERVER
MIGRATING TO AVALANCHE 5.0 WITH MS SQL SERVER This document provides instructions for migrating to Avalanche 5.0 from an installation of Avalanche MC 4.6 or newer using MS SQL Server 2005. You can continue
More informationOperationalise Predictive Analytics
Operationalise Predictive Analytics Publish SPSS, Excel and R reports online Predict online using SPSS and R models Access models and reports via Android app Organise people and content into projects Monitor
More informationInternational Journal of Engineering Research ISSN: 2348-4039 & Management Technology November-2015 Volume 2, Issue-6
International Journal of Engineering Research ISSN: 2348-4039 & Management Technology Email: editor@ijermt.org November-2015 Volume 2, Issue-6 www.ijermt.org Modeling Big Data Characteristics for Discovering
More informationDATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.
DATA MINING TECHNOLOGY Georgiana Marin 1 Abstract In terms of data processing, classical statistical models are restrictive; it requires hypotheses, the knowledge and experience of specialists, equations,
More informationANALYTICS CENTER LEARNING PROGRAM
Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals
More informationConfiguring an Alternative Database for SAS Web Infrastructure Platform Services
Configuration Guide Configuring an Alternative Database for SAS Web Infrastructure Platform Services By default, SAS Web Infrastructure Platform Services is configured to use SAS Framework Data Server.
More informationMicrosoft SharePoint Architectural Models
Microsoft SharePoint This topic is 1 of 5 in a series Introduction to Fundamental SharePoint This series is intended to raise awareness of the different fundamental architectural models through which SharePoint
More informationWEBAPP PATTERN FOR APACHE TOMCAT - USER GUIDE
WEBAPP PATTERN FOR APACHE TOMCAT - USER GUIDE Contents 1. Pattern Overview... 3 Features 3 Getting started with the Web Application Pattern... 3 Accepting the Web Application Pattern license agreement...
More informationPlease contact Cyber and Technology Training at (410)777-1333/technologytraining@aacc.edu for registration and pricing information.
Course Name Start Date End Date Start Time End Time Active Directory Services with Windows Server 8/31/2015 9/4/2015 9:00 AM 5:00 PM Active Directory Services with Windows Server 9/28/2015 10/2/2015 9:00
More informationAzure Machine Learning, SQL Data Mining and R
Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:
More informationIn-Database Analytics
Embedding Analytics in Decision Management Systems In-database analytics offer a powerful tool for embedding advanced analytics in a critical component of IT infrastructure. James Taylor CEO CONTENTS Introducing
More informationVisualization of Semantic Windows with SciDB Integration
Visualization of Semantic Windows with SciDB Integration Hasan Tuna Icingir Department of Computer Science Brown University Providence, RI 02912 hti@cs.brown.edu February 6, 2013 Abstract Interactive Data
More informationDatabase Management System as a Cloud Service
Database Management System as a Cloud Service Yvette E. Gelogo 1 and Sunguk Lee 2 * 1 Society of Science and Engineering Research Support, Korea vette_mis@yahoo.com 2 Research Institute of Industrial Science
More informationVery Large Enterprise Network, Deployment, 25000+ Users
Very Large Enterprise Network, Deployment, 25000+ Users Websense software can be deployed in different configurations, depending on the size and characteristics of the network, and the organization s filtering
More informationEnterprise level security, the Huddle way.
Enterprise level security, the Huddle way. Security whitepaper TABLE OF CONTENTS 5 Huddle s promise Hosting environment Network infrastructure Multiple levels of security Physical security System & network
More informationKnowledge Discovery from patents using KMX Text Analytics
Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers
More informationIntended status: Standards Track October 8, 2014 Expires: April 11, 2015
Independent Submission P. Lambert, Ed. Internet-Draft Dendory Networks Intended status: Standards Track October 8, 2014 Expires: April 11, 2015 Abstract ODBC URI Scheme draft 00 This Internet-Draft document
More informationBringing Big Data Modelling into the Hands of Domain Experts
Bringing Big Data Modelling into the Hands of Domain Experts David Willingham Senior Application Engineer MathWorks david.willingham@mathworks.com.au 2015 The MathWorks, Inc. 1 Data is the sword of the
More informationSQL Server Instance-Level Benchmarks with DVDStore
SQL Server Instance-Level Benchmarks with DVDStore Dell developed a synthetic benchmark tool back that can run benchmark tests against SQL Server, Oracle, MySQL, and PostgreSQL installations. It is open-sourced
More informationInvestment Portfolio Performance Evaluation. Jay Patel (jayp@seas.upenn.edu) Faculty Advisor: James Gee
Investment Portfolio Performance Evaluation Jay Patel (jayp@seas.upenn.edu) Faculty Advisor: James Gee April 18, 2008 Abstract Most people who contract with financial managers do not have much understanding
More informationAbout This Document 3. About the Migration Process 4. Requirements and Prerequisites 5. Requirements... 5 Prerequisites... 5
Contents About This Document 3 About the Migration Process 4 Requirements and Prerequisites 5 Requirements... 5 Prerequisites... 5 Installing the Migration Tool and Enabling Migration 8 On Linux Servers...
More informationName: Srinivasan Govindaraj Title: Big Data Predictive Analytics
Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics Please note the following IBM s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice
More informationTackling Big Data with MATLAB Adam Filion Application Engineer MathWorks, Inc.
Tackling Big Data with MATLAB Adam Filion Application Engineer MathWorks, Inc. 2015 The MathWorks, Inc. 1 Challenges of Big Data Any collection of data sets so large and complex that it becomes difficult
More informationEnterprise Network Deployment, 10,000 25,000 Users
Enterprise Network Deployment, 10,000 25,000 Users Websense software can be deployed in different configurations, depending on the size and characteristics of the network, and the organization s filtering
More informationMS 20465C: Designing a Data Solution with Microsoft SQL Server
MS 20465C: Designing a Data Solution with Microsoft SQL Server Description: Note: Days: 5 Prerequisites: The focus of this five-day instructor-led course is on planning and implementing enterprise database
More informationZmanda Cloud Backup Frequently Asked Questions
Zmanda Cloud Backup Frequently Asked Questions Release 4.1 Zmanda, Inc Table of Contents Terminology... 4 What is Zmanda Cloud Backup?... 4 What is a backup set?... 4 What is amandabackup user?... 4 What
More informationCourse 6234A: Implementing and Maintaining Microsoft SQL Server 2008 Analysis Services
Course 6234A: Implementing and Maintaining Microsoft SQL Server 2008 Analysis Services Length: Delivery Method: 3 Days Instructor-led (classroom) About this Course Elements of this syllabus are subject
More informationMaster of Science in Healthcare Informatics and Analytics Program Overview
Master of Science in Healthcare Informatics and Analytics Program Overview The program is a 60 credit, 100 week course of study that is designed to graduate students who: Understand and can apply the appropriate
More informationDFW Backup Software. Whitepaper DFW Backup Agent
Version 6 Jan 2012 Table of Content 1 Introduction... 3 2 DFW Backup Backup Agents... 4 2.1 Microsoft Exchange... 4 2.2 Microsoft SQL Server... 5 2.3 Lotus Domino/s... 6 2.4 Oracle Database... 7 2.5 MySQL
More informationSynchronizer Installation
Synchronizer Installation Synchronizer Installation Synchronizer Installation This document provides instructions for installing Synchronizer. Synchronizer performs all the administrative tasks for XenClient
More informationBig Data and Privacy. Fritz Henglein Dept. of Computer Science, University of Copenhagen. Finance IT Day Riga, 2015-03-26
Big Data and Privacy Fritz Henglein Dept. of Computer Science, University of Copenhagen Finance IT Day Riga, 2015-03-26 About me Professor, Programming Languages and Systems, University of Copenhagen Director,
More informationFight fire with fire when protecting sensitive data
Fight fire with fire when protecting sensitive data White paper by Yaniv Avidan published: January 2016 In an era when both routine and non-routine tasks are automated such as having a diagnostic capsule
More informationCS 564: DATABASE MANAGEMENT SYSTEMS
Fall 2013 CS 564: DATABASE MANAGEMENT SYSTEMS 9/4/13 CS 564: Database Management Systems, Jignesh M. Patel 1 Teaching Staff Instructor: Jignesh Patel, jignesh@cs.wisc.edu Office Hours: Mon, Wed 1:30-2:30
More informationZoner Online Backup. Whitepaper Zoner Backup Agent
Version 5.x Aug 2008 Table of Content 1 Introduction... 3 2 Zoner Backup Agents... 4 2.1 Microsoft Exchange... 4 2.2 Microsoft SQL Server... 5 2.3 Lotus Domino/s... 6 2.4 Oracle Database... 7 2.5 MySQL
More informationHurwitz ValuePoint: Predixion
Predixion VICTORY INDEX CHALLENGER Marcia Kaufman COO and Principal Analyst Daniel Kirsch Principal Analyst The Hurwitz Victory Index Report Predixion is one of 10 advanced analytics vendors included in
More informationTECHNIQUES FOR OPTIMIZING THE RELATIONSHIP BETWEEN DATA STORAGE SPACE AND DATA RETRIEVAL TIME FOR LARGE DATABASES
Techniques For Optimizing The Relationship Between Data Storage Space And Data Retrieval Time For Large Databases TECHNIQUES FOR OPTIMIZING THE RELATIONSHIP BETWEEN DATA STORAGE SPACE AND DATA RETRIEVAL
More informationScaling DBMail with MySQL
Scaling DBMail with MySQL Guido A.J. Stevens 2007 NFG Net Facilities Group All rights reserved http://www.dbmail.eu sales@dbmail.eu T +31.43.3618933 F +31.43.3561655 Scaling DBMail DBMail is a fast, scalable,
More informationMicrosoft Power BI. Nov 21, 2015
Nov 21, 2015 Microsoft Power BI Biray Giray Practice Lead - Enterprise Architecture, Collaboration, ECM, Information Architecture and Governance getalbert.ca biray.giray@getalbert.ca Michael McKiernan
More informationImplementing and Maintaining Microsoft SQL Server 2008 Integration Services
Course 6234A: Implementing and Maintaining Microsoft SQL Server 2008 Integration Services Length: 3 Days Language(s): English Audience(s): IT Professionals Level: 200 Technology: Microsoft SQL Server 2008
More informationAhsay Backup Software. Whitepaper Ahsay Backup Agent
Version 6 Oct 2011 Table of Content 1 Introduction...3 2 Ahsay Backup Agents...4 2.1 Microsoft Exchange...4 2.2 Microsoft SQL Server...4 2.3 Lotus Domino/s...5 2.4 Oracle Database...6 2.5 MySQL Database...7
More informationSQL Server Business Intelligence
SQL Server Business Intelligence Setup and Configuration Guide Himanshu Gupta Technology Solutions Professional Data Platform Contents 1. OVERVIEW... 3 2. OBJECTIVES... 3 3. ASSUMPTIONS... 4 4. CONFIGURE
More informationAuburn Montgomery. Registration and Security Policy for AUM Servers
Auburn Montgomery Title: Responsible Office: Registration and Security Policy for AUM Servers Information Technology Services I. PURPOSE To outline the steps required to register and maintain departmental
More informationIT-Pruefungen.de. Hochwertige Qualität, neueste Prüfungsunterlagen. http://www.it-pruefungen.de
IT-Pruefungen.de Hochwertige Qualität, neueste Prüfungsunterlagen http://www.it-pruefungen.de Exam : 70-452 Title : PRO:MS SQL Server@ 2008, Designing a Business Intelligence Version : Demo 1. You design
More information10775 Administering Microsoft SQL Server Databases
10775 Administering Microsoft SQL Server Databases Course Number: 10775 Category: Microsoft SQL Server 2012 Duration: 5 days Certification: Exam 70-462 Administering Microsoft SQL Server 2012 Databases
More informationAdvanced In-Database Analytics
Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??
More informationData Mining and Data Warehousing on US Farmer s Data
Data Mining and Data Warehousing on US Farmer s Data Guide: Dr. Meiliu Lu Presented By, Yogesh Isawe Kalindi Mehta Aditi Kulkarni * Data Warehousing Project * Introduction * Background * Technologies Explored
More informationSession 15 OF, Unpacking the Actuary's Technical Toolkit. Moderator: Albert Jeffrey Moore, ASA, MAAA
Session 15 OF, Unpacking the Actuary's Technical Toolkit Moderator: Albert Jeffrey Moore, ASA, MAAA Presenters: Melissa Boudreau, FCAS Albert Jeffrey Moore, ASA, MAAA Christopher Kenneth Peek Yonasan Schwartz,
More informationDetecting (and even preventing) SQL Injection Using the Percona Toolkit and Noinject!
Detecting (and even preventing) SQL Injection Using the Percona Toolkit and Noinject! Justin Swanhart Percona Live, April 2013 INTRODUCTION 2 Introduction 3 Who am I? What do I do? Why am I here? The tools
More informationAugmented Search for Software Testing
Augmented Search for Software Testing For Testers, Developers, and QA Managers New frontier in big log data analysis and application intelligence Business white paper May 2015 During software testing cycles,
More informationVery Large Enterprise Network Deployment, 25,000+ Users
Very Large Enterprise Network Deployment, 25,000+ Users Websense software can be deployed in different configurations, depending on the size and characteristics of the network, and the organization s filtering
More informationAnalyzing HTTP/HTTPS Traffic Logs
Advanced Threat Protection Automatic Traffic Log Analysis APTs, advanced malware and zero-day attacks are designed to evade conventional perimeter security defenses. Today, there is wide agreement that
More information2011 Cyber Security and the Advanced Persistent Threat A Holistic View
2011 Cyber and the Advanced Persistent Threat A Holistic View Thomas Varney Cybersecurity & Privacy BM Global Business Services 1 31/10/11 Agenda The Threat We Face A View to Addressing the Four Big Problem
More informationManaging Incompleteness, Complexity and Scale in Big Data
Managing Incompleteness, Complexity and Scale in Big Data Nick Duffield Electrical and Computer Engineering Texas A&M University http://nickduffield.net/work Three Challenges for Big Data Complexity Problem:
More informationGrow Revenues and Reduce Risk with Powerful Analytics Software
Grow Revenues and Reduce Risk with Powerful Analytics Software Overview Gaining knowledge through data selection, data exploration, model creation and predictive action is the key to increasing revenues,
More informationThe Cyber Threat Profiler
Whitepaper The Cyber Threat Profiler Good Intelligence is essential to efficient system protection INTRODUCTION As the world becomes more dependent on cyber connectivity, the volume of cyber attacks are
More informationConnecting to your Database!... 3
Connecting to your Database!... 3 Connecting to Access Databases!... 3 Connecting to SQL Server!... 8 Connecting to Oracle!... 10 Connecting to MySQL!... 11 Connecting to Sybase!... 12 Connecting to IBM
More informationPivotalR: A Package for Machine Learning on Big Data
PivotalR: A Package for Machine Learning on Big Data Hai Qian Predictive Analytics Team, Pivotal Inc. madlib@gopivotal.com Copyright 2013 Pivotal. All rights reserved. What Can Small Data Scientists Bring
More informationNOVA COLLEGE-WIDE COURSE CONTENT SUMMARY ITE 115 - INTRODUCTION TO COMPUTER APPLICATIONS & CONCEPTS (3 CR.)
Revised 5/2010 NOVA COLLEGE-WIDE COURSE CONTENT SUMMARY ITE 115 - INTRODUCTION TO COMPUTER APPLICATIONS & CONCEPTS (3 CR.) Course Description Covers computer concepts and Internet skills and uses a software
More information(Big) Data Anonymization Claude Castelluccia Inria, Privatics
(Big) Data Anonymization Claude Castelluccia Inria, Privatics BIG DATA: The Risks Singling-out/ Re-Identification: ADV is able to identify the target s record in the published dataset from some know information
More informationUsing RADIUS Agent for Transparent User Identification
Using RADIUS Agent for Transparent User Identification Using RADIUS Agent Web Security Solutions Version 7.7, 7.8 Websense RADIUS Agent works together with the RADIUS server and RADIUS clients in your
More informationSecure Cross Border File Protection & Sharing for Enterprise Product Brief CRYPTOMILL INC
C NNECTED Circles of Trust Secure Cross Border File Protection & Sharing for Enterprise Product Brief www.cryptomill.com product overview OVERVIEW Connected Circles of Trust is an endpoint data security
More informationSecuring the Database Stack
Technical Brief Securing the Database Stack How ScaleArc Benefits the Security Team Introduction Relational databases store some of the world s most valuable information, including financial transactions,
More informationWhitepaper FailSafeSolutions Backup Agent
Version 6 Oct 20122 Table of Content 1 Introduction... 3 2 FailSafeSolutions Backup Agents... 4 2.1 Microsoft Exchange... 4 2.2 Microsoft SQL Server... 5 2.3 Lotus Domino/s... 6 2.4 Oracle Database...
More informationAutomating FP&A Analytics Using SAP Visual Intelligence and Predictive Analysis
September 9 11, 2013 Anaheim, California Automating FP&A Analytics Using SAP Visual Intelligence and Predictive Analysis Varun Kumar Learning Points Create management insight tool using SAP Visual Intelligence
More informationIntroduction to Logistic Regression
OpenStax-CNX module: m42090 1 Introduction to Logistic Regression Dan Calderon This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract Gives introduction
More informationBlaze Vault Online Backup. Whitepaper Blaze Vault Online Backup Agent
Blaze Vault Online Backup Whitepaper Blaze Vault Online Backup Agent Version 5.x Jun 2006 Table of Content 1 Introduction... 3 2 Blaze Vault Online Backup Agents... 4 2.1 Microsoft Exchange... 4 2.2 Microsoft
More informationFrom Raw Data to. Actionable Insights with. MATLAB Analytics. Learn more. Develop predictive models. 1Access and explore data
100 001 010 111 From Raw Data to 10011100 Actionable Insights with 00100111 MATLAB Analytics 01011100 11100001 1 Access and Explore Data For scientists the problem is not a lack of available but a deluge.
More informationHOW TO: Using Big Data Analytics to understand how your SharePoint Intranet is being used
HOW TO: Using Big Data Analytics to understand how your SharePoint Intranet is being used About the author: Wim De Groote is Expert Leader Entreprise Collaboration Management at Sogeti. You can reach him
More informationMining Large Datasets: Case of Mining Graph Data in the Cloud
Mining Large Datasets: Case of Mining Graph Data in the Cloud Sabeur Aridhi PhD in Computer Science with Laurent d Orazio, Mondher Maddouri and Engelbert Mephu Nguifo 16/05/2014 Sabeur Aridhi Mining Large
More informationBig Data and Big Analytics
Big Data and Big Analytics Introducing SciDB Open source, massively parallel DBMS and analytic platform Array data model (rather than SQL, Unstructured, XML, or triple-store) Extensible micro-kernel architecture
More informationOPAS Prerequisites. Prepared By: This document contains the prerequisites and requirements for setting up OPAS.
OPAS Prerequisites This document contains the prerequisites and requirements for setting up OPAS. Prepared By: Luke Swords Principal Consultant 24/06/2015 Version 1.0 Contact Information Infront Consulting
More information