Privacy Techniques for Big Data

Size: px
Start display at page:

Download "Privacy Techniques for Big Data"

Transcription

1 Privacy Techniques for Big Data The Pros and Cons of Syntatic and Differential Privacy Approaches Dr#Roksana#Boreli# SMU,#Singapore,#May#2015# Introductions NICTA Australia s National Centre of Excellence in Information and Communication Technology 700 staff, ~ 300 PhD students Presenter Research Leader, Mobile Systems research group Interests: Privacy enhancing technologies, wireless comms and network protocols VRL ATP, NRL CRL QRL 2

2 Outline Privacy De-identification techniques Experiences from a recently completed project Evaluating the privacy utility trade-offs Question time 3 Personal data is collected by services and apps Used for targeted advertising 4

3 The growing importance of privacy Regulatory environment: legislation protecting personal data (PII) collection, storage, use Consumer attitudes to privacy increasing awareness Media attention high potential for negative publicity Maximise the opportunity: how to minimise the risks while preserving data utility for analytics 5 The meaning of PII Privacy regulations based on PII personal vs non-personal information Numerous examples of re-identification of anonymised data The falacy of PII - Any information could be PII, and should be protected PII : Personally identifiable information 6

4 Regulatory guidelines for de-identification Australian guidelines Frequency and dominance rules for data aggregates US HIPAA regulation (health data) 1. Redact identifiers 2. Generalise (mask) location and dates 3. Residual information should not lead to re-identification The Health Insurance Portability and Accountability Act of 1996 (HIPAA), Safe Harbor method 7 Netflix privacy breach Dataset for Netflix Prize contest: 17,770 movie titles 480,189 users with random customer IDs Ratings: 1-5 For each movie we have the ratings: (MovieID, CustomerID, Rating, Date) Given auxiliary information (random chats, IMDB), recommender identity can be uncovered with high probability. Robust De-anonymization of Large Sparse Datasets, Narayanan and Shmatikov,

5 De-identifying mobility data Based on analysis of anonymised call information of ~1.5 million users in a western country (1 hour precision): * 4 spatio-temporal points are enough to uniquely identify of the individuals. 95% Yves-Alexandre de Montjoye, César A. Hidalgo, Michel Verleysen and Vincent D. Blondel, Unique in the Crowd: the privacy bounds of human mobility. Scientific Reports 3:1376, March Available on 9 Technologies from research domain 1. Anonymisation 2. Obfuscation, Differential privacy 3. Cryptographic solutions More on-going research topics 10

6 Anonymisation Scenario: storage or release of private information Simple: removal of PII and sensitive information Easy to reverse, using side information Name! Gender! Age! Oliver Brown# Emily Taylor# William Walker# Post code! Monthly bill! Male# 43# 3067# $198# Female# 37# 3040# $45# Male# 19# 3825# $146# Jack Harris# Male# 26# 3028# $35# Emma Anderson# Female# 42# 3195# $30# Lily White# Female# 55# 3067# $72# Lucas Johnson# Customer data Male# 59# 3818# $79# Name! Gender! Age! Post code! Jane Eyre# Female# 43# 2066# Emily Taylor# Female# 37# 3040# Rob Reed# Male# 59# 2100# Jack Johnson# Census data Male# 48# 3860# 11 Anonymisation: Syntatic approaches data set based rules Frequency rule Each aggregation result is derived from (min) k records and +confidentialise+data:+the+basic+principles 12

7 Anonymisation: Syntatic approaches k-anonymity Any unique combination of selected attributes/ features can belong to a min group of k users 476** 2* * Male Post code Age Gender Female L. Sweeney. k-anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10 (7), k-anonymity (k=2) Gender! Age! Post code! Monthly bill! Male# -# 30**# $198# Female# -# 30**# $45# Male# -# 38**# $146# Male# -# 30**# $35# Female# -# 31**# $30# Female# -# 30**# $72# Male# -# 38**# $79# Female# -# 31**# $121# Male# -# 38**# $82# Female# -# 31**# $155# l-diversity: within the group of k, ensure that there is a mix of specific values of sensitive attribute t-closeness, etc.

8 Data obfuscation Scenario: releasing aggregate data information Differential privacy requires that computations be insensitive to changes in any particular individual's record. Consequently, being opted in or out of the database should make little difference to a person s privacy. AΔB=1## A" B" M" M" M(A)" # M(B)" 15 Differential privacy Add calibrated noise to sensitive data: e.g. generated by Laplace function ε parameter ~ privacy strength Name! Gender! Age! Post code! Monthly bill! Oliver Male $198 Brown Emily Female $45 Taylor Average bill: $ noise => Average bill: $91.4 C. Dwork. Differential privacy. In ICALP (2), pages 1 12, 2006 C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In Proc. of the 3rd TCC, pages ,

9 Evaluating the utility of privacy techniques Industry collaboration project POC implementation of selected privacy techniques Redaction (masking), anonymisation and obfuscation Evaluate the solution for a set of analytics scenarios: quantify utility and privacy Approach Use cases (Analytics) Data sets original after PET Privacy mechanisms Metrics Analytics: RMSE, MRE, Lift Compared to the results using original data Privacy: uniqueness, entropy, diff privacy ε 18

CS346: Advanced Databases

CS346: Advanced Databases CS346: Advanced Databases Alexandra I. Cristea A.I.Cristea@warwick.ac.uk Data Security and Privacy Outline Chapter: Database Security in Elmasri and Navathe (chapter 24, 6 th Edition) Brief overview of

More information

Challenges of Data Privacy in the Era of Big Data. Rebecca C. Steorts, Vishesh Karwa Carnegie Mellon University November 18, 2014

Challenges of Data Privacy in the Era of Big Data. Rebecca C. Steorts, Vishesh Karwa Carnegie Mellon University November 18, 2014 Challenges of Data Privacy in the Era of Big Data Rebecca C. Steorts, Vishesh Karwa Carnegie Mellon University November 18, 2014 1 Outline Why should we care? What is privacy? How do achieve privacy? Big

More information

Big Data and Consumer Privacy in the Internet Economy 79 Fed. Reg. 32714 (Jun. 6, 2014)

Big Data and Consumer Privacy in the Internet Economy 79 Fed. Reg. 32714 (Jun. 6, 2014) Big Data and Consumer Privacy in the Internet Economy 79 Fed. Reg. 32714 (Jun. 6, 2014) Comment of Solon Barocas, Edward W. Felten, Joanna N. Huey, Joshua A. Kroll, and Arvind Narayanan Thank you for the

More information

Policy-based Pre-Processing in Hadoop

Policy-based Pre-Processing in Hadoop Policy-based Pre-Processing in Hadoop Yi Cheng, Christian Schaefer Ericsson Research Stockholm, Sweden yi.cheng@ericsson.com, christian.schaefer@ericsson.com Abstract While big data analytics provides

More information

Privacy by Design für Big Data

Privacy by Design für Big Data Dr. Günter Karjoth 26. August 2013 Sommerakademie Kiel Privacy by Design für Big Data 1 / 34 2013 IBM Coorporation Privacy by Design (PbD) proposed by Ann Cavoukin, Privacy Commissioner Ontario mostly

More information

(Big) Data Anonymization Claude Castelluccia Inria, Privatics

(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 information

Privacy Challenges of Telco Big Data

Privacy Challenges of Telco Big Data Dr. Günter Karjoth June 17, 2014 ITU telco big data workshop Privacy Challenges of Telco Big Data Mobile phones are great sources of data but we must be careful about privacy 1 / 15 Sources of Big Data

More information

Big Data and Innovation, Setting the Record Straight: De-identification Does Work

Big Data and Innovation, Setting the Record Straight: De-identification Does Work Big Data and Innovation, Setting the Record Straight: De-identification Does Work Ann Cavoukian, Ph.D. Information and Privacy Commissioner Daniel Castro Senior Analyst, Information Technology and Innovation

More information

Principles and Best Practices for Sharing Data from Environmental Health Research: Challenges Associated with Data-Sharing: HIPAA De-identification

Principles and Best Practices for Sharing Data from Environmental Health Research: Challenges Associated with Data-Sharing: HIPAA De-identification Principles and Best Practices for Sharing Data from Environmental Health Research: Challenges Associated with Data-Sharing: HIPAA De-identification Daniel C. Barth-Jones, M.P.H., Ph.D Assistant Professor

More information

White Paper. The Definition of Persona Data: Seeing the Complete Spectrum

White Paper. The Definition of Persona Data: Seeing the Complete Spectrum White Paper The Definition of Persona Data: Seeing the Complete Spectrum Omer Tene, Senior Fellow Christopher Wolf, Founder and Co- Chair The Future of Privacy Forum January 2013 The Future of Privacy

More information

ARTICLE 29 DATA PROTECTION WORKING PARTY

ARTICLE 29 DATA PROTECTION WORKING PARTY ARTICLE 29 DATA PROTECTION WORKING PARTY 0829/14/EN WP216 Opinion 05/2014 on Anonymisation Techniques Adopted on 10 April 2014 This Working Party was set up under Article 29 of Directive 95/46/EC. It is

More information

Privacy Committee. Privacy and Open Data Guideline. Guideline. Of South Australia. Version 1

Privacy Committee. Privacy and Open Data Guideline. Guideline. Of South Australia. Version 1 Privacy Committee Of South Australia Privacy and Open Data Guideline Guideline Version 1 Executive Officer Privacy Committee of South Australia c/o State Records of South Australia GPO Box 2343 ADELAIDE

More information

De-Identification 101

De-Identification 101 De-Identification 101 We live in a world today where our personal information is continuously being captured in a multitude of electronic databases. Details about our health, financial status and buying

More information

Discussion on papers on anonymisation

Discussion on papers on anonymisation Discussion on papers on anonymisation Josep Domingo-Ferrer 1 and Aleksandra Slavkovic 2 1 Universitat Rovira i Virgili, Tarragona josep.domingo@urv.cat 2 Pennsylvania State University sesa@stat.psu.edu

More information

Differentially private client-side data deduplication protocol for cloud storage services

Differentially private client-side data deduplication protocol for cloud storage services SECURITY AND COMMUNICATION NETWORKS Security Comm. Networks (2014) Published online in Wiley Online Library (wileyonlinelibrary.com)..1159 RESEARCH ARTICLE Differentially private client-side data deduplication

More information

RE-IDENTIFICATION RISK IN SWAPPED MICRODATA RELEASE

RE-IDENTIFICATION RISK IN SWAPPED MICRODATA RELEASE RE-IDENTIFICATION RISK IN SWAPPED MICRODATA RELEASE Krish Muralidhar, Department of Marketing and Supply Chain Management, University of Oklahoma, Norman, OK 73019, (405)-325-2677, krishm@ou.edu ABSTRACT

More information

future proof data privacy

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 information

Privacy and Data-Based Research

Privacy and Data-Based Research Journal of Economic Perspectives Volume 28, Number 2 Spring 2014 Pages 75 98 Privacy and Data-Based Research Ori Heffetz and Katrina Ligett On n August 9, 2006, the Technology section of the New York Times

More information

De-Identification of Clinical Data

De-Identification of Clinical Data De-Identification of Clinical Data Sepideh Khosravifar, CISSP Info Security Analyst IV Tyrone Grandison, PhD Manager, Privacy Research, IBM TEPR Conference 2008 Ft. Lauderdale, Florida May 17-21, 2008

More information

Probabilistic Prediction of Privacy Risks

Probabilistic Prediction of Privacy Risks Probabilistic Prediction of Privacy Risks in User Search Histories Joanna Biega Ida Mele Gerhard Weikum PSBD@CIKM, Shanghai, 07.11.2014 Or rather: On diverging towards user-centric privacy Traditional

More information

De-identification, defined and explained. Dan Stocker, MBA, MS, QSA Professional Services, Coalfire

De-identification, defined and explained. Dan Stocker, MBA, MS, QSA Professional Services, Coalfire De-identification, defined and explained Dan Stocker, MBA, MS, QSA Professional Services, Coalfire Introduction This perspective paper helps organizations understand why de-identification of protected

More information

Protecting Patient Privacy. Khaled El Emam, CHEO RI & uottawa

Protecting Patient Privacy. Khaled El Emam, CHEO RI & uottawa Protecting Patient Privacy Khaled El Emam, CHEO RI & uottawa Context In Ontario data custodians are permitted to disclose PHI without consent for public health purposes What is the problem then? This disclosure

More information

Tomislav Križan Consultancy Poslovna Inteligencija d.o.o

Tomislav Križan Consultancy Poslovna Inteligencija d.o.o In-Situ Anonymization of Big Data Tomislav Križan Consultancy Director @ Poslovna Inteligencija d.o.o Abstract Vast amount of data is being generated from versatile sources and organizations are primarily

More information

DRAFT NISTIR 8053 De-Identification of Personally Identifiable Information

DRAFT NISTIR 8053 De-Identification of Personally Identifiable Information 1 2 3 4 5 6 7 8 DRAFT NISTIR 8053 De-Identification of Personally Identifiable Information Simson L. Garfinkel 9 10 11 12 13 14 15 16 17 18 NISTIR 8053 DRAFT De-Identification of Personally Identifiable

More information

Risk management, information security and privacy compliance. new meeting of minds or ships in the night?

Risk management, information security and privacy compliance. new meeting of minds or ships in the night? Risk management, information security and privacy compliance new meeting of minds or ships in the night? Peter Leonard September 2015 page 1 ships in the night + narrowly focussed conversations reasonable

More information

De-identification Koans. ICTR Data Managers Darren Lacey January 15, 2013

De-identification Koans. ICTR Data Managers Darren Lacey January 15, 2013 De-identification Koans ICTR Data Managers Darren Lacey January 15, 2013 Disclaimer There are several efforts addressing this issue in whole or part Over the next year or so, I believe that the conversation

More information

De-Identification Framework

De-Identification Framework A Consistent, Managed Methodology for the De-Identification of Personal Data and the Sharing of Compliance and Risk Information March 205 Contents Preface...3 Introduction...4 Defining Categories of Health

More information

No silver bullet: De-identification still doesn't work

No silver bullet: De-identification still doesn't work No silver bullet: De-identification still doesn't work Arvind Narayanan arvindn@cs.princeton.edu Edward W. Felten felten@cs.princeton.edu July 9, 2014 Paul Ohm s 2009 article Broken Promises of Privacy

More information

PrivacyCanary: Privacy-Aware Recommenders with Adaptive Input Obfuscation

PrivacyCanary: Privacy-Aware Recommenders with Adaptive Input Obfuscation PrivacyCanary: Privacy-Aware Recommenders with Adaptive Input Obfuscation Thivya Kandappu, Arik Friedman, Roksana Boreli, Vijay Sivaraman School of Electrical Engineering & Telecommunications, UNSW, Sydney,

More information

How to De-identify Data. Xulei Shirley Liu Department of Biostatistics Vanderbilt University 03/07/2008

How to De-identify Data. Xulei Shirley Liu Department of Biostatistics Vanderbilt University 03/07/2008 How to De-identify Data Xulei Shirley Liu Department of Biostatistics Vanderbilt University 03/07/2008 1 Outline The problem Brief history The solutions Examples with SAS and R code 2 Background The adoption

More information

Data De-identification and Anonymization of Individual Patient Data in Clinical Studies A Model Approach

Data De-identification and Anonymization of Individual Patient Data in Clinical Studies A Model Approach Data De-identification and Anonymization of Individual Patient Data in Clinical Studies A Model Approach Background TransCelerate BioPharma Inc. is a non-profit organization of biopharmaceutical companies

More information

Privacy Preserving Data Mining

Privacy Preserving Data Mining Privacy Preserving Data Mining Technion - Computer Science Department - Ph.D. Thesis PHD-2011-01 - 2011 Arie Friedman Privacy Preserving Data Mining Technion - Computer Science Department - Ph.D. Thesis

More information

Comments of the World Privacy Forum To: Office of Science and Technology Policy Re: Big Data Request for Information. Via email to bigdata@ostp.

Comments of the World Privacy Forum To: Office of Science and Technology Policy Re: Big Data Request for Information. Via email to bigdata@ostp. 3108 Fifth Avenue Suite B San Diego, CA 92103 Comments of the World Privacy Forum To: Office of Science and Technology Policy Re: Big Data Request for Information Via email to bigdata@ostp.gov Big Data

More information

Societal benefits vs. privacy: what distributed secure multi-party computation enable? Research ehelse 2015 21-22 April Oslo

Societal benefits vs. privacy: what distributed secure multi-party computation enable? Research ehelse 2015 21-22 April Oslo Privacy Societal benefits vs. privacy: what distributed secure multi-party computation enable? Research ehelse 2015 21-22 April Oslo Kassaye Yitbarek Yigzaw UiT The Arctic University of Norway Outline

More information

The De-identification of Personally Identifiable Information

The De-identification of Personally Identifiable Information The De-identification of Personally Identifiable Information Khaled El Emam (PhD) www.privacyanalytics.ca 855.686.4781 info@privacyanalytics.ca 251 Laurier Avenue W, Suite 200 Ottawa, ON Canada K1P 5J6

More information

Guidance on De-identification of Protected Health Information November 26, 2012.

Guidance on De-identification of Protected Health Information November 26, 2012. Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule November 26, 2012 OCR gratefully

More information

De-Identification of Health Data under HIPAA: Regulations and Recent Guidance" " "

De-Identification of Health Data under HIPAA: Regulations and Recent Guidance  De-Identification of Health Data under HIPAA: Regulations and Recent Guidance" " " D even McGraw " Director, Health Privacy Project January 15, 201311 HIPAA Scope Does not cover all health data Applies

More information

Degrees of De-identification of Clinical Research Data

Degrees of De-identification of Clinical Research Data Vol. 7, No. 11, November 2011 Can You Handle the Truth? Degrees of De-identification of Clinical Research Data By Jeanne M. Mattern Two sets of U.S. government regulations govern the protection of personal

More information

Privacy & data protection in big data: Fact or Fiction?

Privacy & data protection in big data: Fact or Fiction? Privacy & data protection in big data: Fact or Fiction? Athena Bourka ENISA ISACA Athens Conference 24.11.2015 European Union Agency for Network and Information Security Agenda 1 Privacy challenges in

More information

Other Privacy Team Members

Other Privacy Team Members Privacy Technologies in the Era of Big Data:! Reflections & New Directions Ersin Uzun, PhD Palo Alto Research Center Other Privacy Team Members Shantanu Rane, PhD Julien Freudiger, PhD Ersin Uzun Director

More information

A Q&A with the Commissioner: Big Data and Privacy Health Research: Big Data, Health Research Yes! Personal Data No!

A Q&A with the Commissioner: Big Data and Privacy Health Research: Big Data, Health Research Yes! Personal Data No! A Q&A with the Commissioner: Big Data and Privacy Health Research: Big Data, Health Research Yes! Personal Data No! Ann Cavoukian, Ph.D. Information and Privacy Commissioner Ontario, Canada THE AGE OF

More information

Administrative Services

Administrative Services Policy Title: Administrative Services De-identification of Client Information and Use of Limited Data Sets Policy Number: DHS-100-007 Version: 2.0 Effective Date: Upon Approval Signature on File in the

More information

Health Data De-Identification by Dr. Khaled El Emam

Health Data De-Identification by Dr. Khaled El Emam RISK-BASED METHODOLOGY DEFENSIBLE COST-EFFECTIVE DE-IDENTIFICATION OPTIMAL STATISTICAL METHOD REPORTING RE-IDENTIFICATION BUSINESS ASSOCIATES COMPLIANCE HIPAA PHI REPORTING DATA SHARING REGULATORY UTILITY

More information

PRIVACY IMPLICATIONS FOR NEXT GENERATION SIEMs AND OTHER META-SYSTEMS

PRIVACY IMPLICATIONS FOR NEXT GENERATION SIEMs AND OTHER META-SYSTEMS PRIVACY IMPLICATIONS FOR NEXT GENERATION SIEMs AND OTHER META-SYSTEMS www.massif-project.eu Dr Andrew Hutchison T-Systems (andrew.hutchison@t-systems.com) MAanagement of Security information and events

More information

Clinical Study Reports Approach to Protection of Personal Data

Clinical Study Reports Approach to Protection of Personal Data Clinical Study Reports Approach to Protection of Personal Data Background TransCelerate BioPharma Inc. is a non-profit organization of biopharmaceutical companies focused on advancing innovation in research

More information

De-identification Protocols:

De-identification Protocols: De-identification Protocols: Essential for Protecting Privacy Office of the Information and Privacy Commissioner of Ontario, Canada Khaled El Emam, Ph.D. Canada Research Chair in Electronic Health Information

More information

ADVISORY GUIDELINES ON THE PERSONAL DATA PROTECTION ACT FOR SELECTED TOPICS ISSUED BY THE PERSONAL DATA PROTECTION COMMISSION ISSUED 24 SEPTEMBER 2013

ADVISORY GUIDELINES ON THE PERSONAL DATA PROTECTION ACT FOR SELECTED TOPICS ISSUED BY THE PERSONAL DATA PROTECTION COMMISSION ISSUED 24 SEPTEMBER 2013 ADVISORY GUIDELINES ON THE PERSONAL DATA PROTECTION ACT FOR SELECTED TOPICS ISSUED BY THE PERSONAL DATA PROTECTION COMMISSION ISSUED 24 SEPTEMBER 2013 REVISED 16 MAY 2014 PART I: INTRODUCTION AND OVERVIEW...

More information

Differentially Private Analysis of

Differentially Private Analysis of Title: Name: Affil./Addr. Keywords: SumOriWork: Differentially Private Analysis of Graphs Sofya Raskhodnikova, Adam Smith Pennsylvania State University Graphs, privacy, subgraph counts, degree distribution

More information

Information Security in Big Data using Encryption and Decryption

Information Security in Big Data using Encryption and Decryption International Research Journal of Computer Science (IRJCS) ISSN: 2393-9842 Information Security in Big Data using Encryption and Decryption SHASHANK -PG Student II year MCA S.K.Saravanan, Assistant Professor

More information

Legal Insight. Big Data Analytics Under HIPAA. Kevin Coy and Neil W. Hoffman, Ph.D. Applicability of HIPAA

Legal Insight. Big Data Analytics Under HIPAA. Kevin Coy and Neil W. Hoffman, Ph.D. Applicability of HIPAA Big Data Analytics Under HIPAA Kevin Coy and Neil W. Hoffman, Ph.D. Privacy laws and regulations such as the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule can have a significant

More information

Privacy Update for Australian Government Agencies. What we've seen in the first 12 months of the new APPs and what's next!

Privacy Update for Australian Government Agencies. What we've seen in the first 12 months of the new APPs and what's next! Privacy Update for Australian Government Agencies What we've seen in the first 12 months of the new APPs and what's next! Presented by Sharon Rowe and Alec Christie Canberra, 31 March 2015 What we are

More information

Privacy-preserving Data-aggregation for Internet-of-things in Smart Grid

Privacy-preserving Data-aggregation for Internet-of-things in Smart Grid Privacy-preserving Data-aggregation for Internet-of-things in Smart Grid Aakanksha Chowdhery Postdoctoral Researcher, Microsoft Research ac@microsoftcom Collaborators: Victor Bahl, Ratul Mahajan, Frank

More information

Combining structured data with machine learning to improve clinical text de-identification

Combining structured data with machine learning to improve clinical text de-identification Combining structured data with machine learning to improve clinical text de-identification DT Tran Scott Halgrim David Carrell Group Health Research Institute Clinical text contains Personally identifiable

More information

Anonymization of Administrative Billing Codes with Repeated Diagnoses Through Censoring

Anonymization of Administrative Billing Codes with Repeated Diagnoses Through Censoring Anonymization of Administrative Billing Codes with Repeated Diagnoses Through Censoring Acar Tamersoy, Grigorios Loukides PhD, Joshua C. Denny MD MS, and Bradley Malin PhD Department of Biomedical Informatics,

More information

Information Sheet: Cloud Computing

Information Sheet: Cloud Computing info sheet 03.11 Information Sheet: Cloud Computing Info Sheet 03.11 May 2011 This Information Sheet gives a brief overview of how the Information Privacy Act 2000 (Vic) applies to cloud computing technologies.

More information

Privacy-Preserving Medical Data Sharing

Privacy-Preserving Medical Data Sharing Privacy-Preserving Medical Data Sharing Aris Gkoulalas-Divanis* arisdiva@ie.ibm.com IBM Research - Ireland Grigorios Loukides* g.loukides@cs.cf.ac.uk Cardiff University SIAM Data Mining, Anaheim, CA, USA,

More information

Yale University Open Data Access (YODA) Project Procedures to Guide External Investigator Access to Clinical Trial Data Last Updated August 2015

Yale University Open Data Access (YODA) Project Procedures to Guide External Investigator Access to Clinical Trial Data Last Updated August 2015 OVERVIEW Yale University Open Data Access (YODA) Project These procedures support the YODA Project Data Release Policy and more fully describe the process by which clinical trial data held by a third party,

More information

A Precautionary Approach to Big Data Privacy

A Precautionary Approach to Big Data Privacy A Precautionary Approach to Big Data Privacy Arvind Narayanan Joanna Huey Edward W. Felten arvindn@cs.princeton.edu jhuey@princeton.edu felten@cs.princeton.edu March 19, 2015 Once released to the public,

More information

NSF Workshop on Big Data Security and Privacy

NSF Workshop on Big Data Security and Privacy NSF Workshop on Big Data Security and Privacy Report Summary Bhavani Thuraisingham The University of Texas at Dallas (UTD) February 19, 2015 Acknowledgement NSF SaTC Program for support Chris Clifton and

More information

The Challenges of Effectively Anonymizing Network Data

The Challenges of Effectively Anonymizing Network Data The Challenges of Effectively Anonymizing Network Data Scott E. Coull Fabian Monrose Michael K. Reiter Michael Bailey Johns Hopkins University Baltimore, MD coulls@cs.jhu.edu University of North Carolina

More information

The Cost of Privacy: Destruction of Data-Mining Utility in Anonymized Data Publishing

The Cost of Privacy: Destruction of Data-Mining Utility in Anonymized Data Publishing The Cost of Privacy: Destruction of Data-Mining Utility in Anonymized Data Publishing Justin Brickell and Vitaly Shmatikov Department of Computer Sciences The University of Texas at Austin jlbrick@cs.utexas.edu,

More information

Data Use Policy. Revision 1.1. 03/09/2014 Ramos M. Mays, Chief Technology Officer

Data Use Policy. Revision 1.1. 03/09/2014 Ramos M. Mays, Chief Technology Officer Data Use Policy Revision 1.1 03/09/2014 Ramos M. Mays, Chief Technology Officer Table of Contents 1. Information Sources... 3 2. Information we receive... 3 3. How we use information... 4 4. How long we

More information

March 31, 2014. Re: Government Big Data (FR Doc. 2014-04660) Dear Ms. Wong:

March 31, 2014. Re: Government Big Data (FR Doc. 2014-04660) Dear Ms. Wong: Microsoft Corporation Tel 425 882 8080 One Microsoft Way Fax 425 936 7329 Redmond, WA 98052-6399 http://www.microsoft.com/ March 31, 2014 Ms. Nicole Wong Big Data Study Office of Science and Technology

More information

Securing Big Data Learning and Differences from Cloud Security

Securing Big Data Learning and Differences from Cloud Security Securing Big Data Learning and Differences from Cloud Security Samir Saklikar RSA, The Security Division of EMC Session ID: DAS-108 Session Classification: Advanced Agenda Cloud Computing & Big Data Similarities

More information

ARX A Comprehensive Tool for Anonymizing Biomedical Data

ARX A Comprehensive Tool for Anonymizing Biomedical Data ARX A Comprehensive Tool for Anonymizing Biomedical Data Fabian Prasser, Florian Kohlmayer, Klaus A. Kuhn Chair of Biomedical Informatics Institute of Medical Statistics and Epidemiology Rechts der Isar

More information

Zubi Advertising Privacy Policy

Zubi Advertising Privacy Policy Zubi Advertising Privacy Policy This privacy policy applies to information collected by Zubi Advertising Services, Inc. ( Company, we or us ), on our Latino Emoji mobile application or via our Latino Emoji

More information

The Information Commissioner s Office response to HM Treasury s Call for Evidence on Data Sharing and Open Data in Banking

The Information Commissioner s Office response to HM Treasury s Call for Evidence on Data Sharing and Open Data in Banking The Information Commissioner s Office response to HM Treasury s Call for Evidence on Data Sharing and Open Data in Banking The Information Commissioner has responsibility for promoting and enforcing the

More information

Decentralizing Privacy: Using Blockchain to Protect Personal Data

Decentralizing Privacy: Using Blockchain to Protect Personal Data Decentralizing Privacy: Using Blockchain to Protect Personal Data Guy Zyskind MIT Media Lab Cambridge, Massachusetts Email: guyz@mit.edu Oz Nathan Tel-Aviv University Tel-Aviv, Israel Email: oznathan@gmail.com

More information

DESTINATION MELBOURNE PRIVACY POLICY

DESTINATION MELBOURNE PRIVACY POLICY DESTINATION MELBOURNE PRIVACY POLICY 2 Destination Melbourne Privacy Policy Statement Regarding Privacy Policy Destination Melbourne Limited recognises the importance of protecting the privacy of personally

More information

On the Effectiveness of Obfuscation Techniques in Online Social Networks

On the Effectiveness of Obfuscation Techniques in Online Social Networks On the Effectiveness of Obfuscation Techniques in Online Social Networks Terence Chen 1,2, Roksana Boreli 1,2, Mohamed-Ali Kaafar 1,3, and Arik Friedman 1,2 1 NICTA, Australia 2 UNSW, Australia 3 INRIA,

More information

Research Data Management Plan (RDMP template)

Research Data Management Plan (RDMP template) DRAFT Research Data Management Plan (RDMP template) The Data Management Plan can be used in a number of ways to assist with data management planning. These include: To identify a series of issues and underlying

More information

MOBILE PHONE NETWORK DATA FOR DEVELOPMENT

MOBILE PHONE NETWORK DATA FOR DEVELOPMENT October 2013 MOBILE PHONE NETWORK DATA FOR DEVELOPMENT How analysis of Call Detail Records (CDRs) provides valuable information for humanitarian development action WHAT ARE CDRs? Whenever a mobile phone

More information

Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks

Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks Modeling Unintended PersonalInformation Leakage from Multiple Online Social Networks Most people have multiple accounts on different social networks. Because these networks offer various levels of privacy

More information

Sharing Data for Public Security

Sharing Data for Public Security Sharing Data for Public Security Michele Bezzi, Gilles Montagnon, Vincent Salzgeber, Slim Trabelsi To cite this version: Michele Bezzi, Gilles Montagnon, Vincent Salzgeber, Slim Trabelsi. Sharing Data

More information

Survey of Research on Information Security in Big Data

Survey of Research on Information Security in Big Data Survey of Research on Information Security in Big Data Zhang Hongjun 1, Hao Wenning 1, He Dengchao 1, Mao Yuxing 1 1 PLA university of Industry and Technology Nan Jing, China hdchao1989@163.com Abstract.

More information

Uncovering Digital Divide and Physical Divide in Senegal Using Mobile Phone Data

Uncovering Digital Divide and Physical Divide in Senegal Using Mobile Phone Data Uncovering Digital Divide and Physical Divide in Senegal Using Mobile Phone Data Song Gao, Bo Yan, Li Gong, Blake Regalia, Yiting Ju, Yingjie Hu STKO Lab, Department of Geography, University of California,

More information

A Survey of Quantification of Privacy Preserving Data Mining Algorithms

A Survey of Quantification of Privacy Preserving Data Mining Algorithms A Survey of Quantification of Privacy Preserving Data Mining Algorithms Elisa Bertino, Dan Lin, and Wei Jiang Abstract The aim of privacy preserving data mining (PPDM) algorithms is to extract relevant

More information

Formal Methods for Preserving Privacy for Big Data Extraction Software

Formal Methods for Preserving Privacy for Big Data Extraction Software Formal Methods for Preserving Privacy for Big Data Extraction Software M. Brian Blake and Iman Saleh Abstract University of Miami, Coral Gables, FL Given the inexpensive nature and increasing availability

More information

A De-identification Strategy Used for Sharing One Data Provider s Oncology Trials Data through the Project Data Sphere Repository

A De-identification Strategy Used for Sharing One Data Provider s Oncology Trials Data through the Project Data Sphere Repository A De-identification Strategy Used for Sharing One Data Provider s Oncology Trials Data through the Project Data Sphere Repository Prepared by: Bradley Malin, Ph.D. 2525 West End Avenue, Suite 1030 Nashville,

More information

SEMANTICS ENABLED PROACTIVE AND TARGETED DISSEMINATION OF NEW MEDICAL KNOWLEDGE

SEMANTICS ENABLED PROACTIVE AND TARGETED DISSEMINATION OF NEW MEDICAL KNOWLEDGE SEMANTICS ENABLED PROACTIVE AND TARGETED DISSEMINATION OF NEW MEDICAL KNOWLEDGE Lakshmish Ramaswamy & I. Budak Arpinar Dept. of Computer Science, University of Georgia laks@cs.uga.edu, budak@cs.uga.edu

More information

Overvie w of Data. Points to Ponder

Overvie w of Data. Points to Ponder 1 Overvie w of Data Anonymiz ation Points to Ponder What is data anonymization? What are the drivers for data anonymization? Here are some startling statistics on security incidents and private data breaches:

More information

We may collect the following types of information during your visit on our Site:

We may collect the following types of information during your visit on our Site: Privacy Policy This Privacy Policy (the Policy ) governs the use and collection of information that Horizon Broadcasting Group, LLC (collectively, "we," "our" or the "website") obtains from you while you

More information

IT Privacy Certification Outline of the Body of Knowledge (BOK) for the Certified Information Privacy Technologist (CIPT)

IT Privacy Certification Outline of the Body of Knowledge (BOK) for the Certified Information Privacy Technologist (CIPT) Page 1 of 6 IT Privacy Certification Outline of the Body of Knowledge (BOK) for the Certified Information Privacy Technologist (CIPT) I. Understanding the need for privacy in the IT environment A. Evolving

More information

Anonymizing Unstructured Data to Enable Healthcare Analytics Chris Wright, Vice President Marketing, Privacy Analytics

Anonymizing Unstructured Data to Enable Healthcare Analytics Chris Wright, Vice President Marketing, Privacy Analytics Anonymizing Unstructured Data to Enable Healthcare Analytics Chris Wright, Vice President Marketing, Privacy Analytics Privacy Analytics - Overview For organizations that want to safeguard and enable their

More information

Privacy-preserving Data Mining: current research and trends

Privacy-preserving Data Mining: current research and trends Privacy-preserving Data Mining: current research and trends Stan Matwin School of Information Technology and Engineering University of Ottawa, Canada stan@site.uottawa.ca Few words about our research Universit[é

More information

Privacy Preserving Health Data Publishing using Secure Two Party Algorithm

Privacy Preserving Health Data Publishing using Secure Two Party Algorithm IJIRST International Journal for Innovative Research in Science & Technology Volume 2 Issue 1 June 2015 ISSN (online): 2349-6010 Privacy Preserving Health Data Publishing using Secure Two Party Algorithm

More information

Influence of Interpersonal Interaction between Public Health Sanitarians and Food Service Establishment Personnel on Food Safety Inspections

Influence of Interpersonal Interaction between Public Health Sanitarians and Food Service Establishment Personnel on Food Safety Inspections Presenters: Michelle Menegay, MPH; Emily Blake, MPH candidate; Scott Frank, MD, MS Affiliation: Case Western Reserve University Title: Influence of Interpersonal Interaction between Public Health Sanitarians

More information

The De-identification Maturity Model Authors: Khaled El Emam, PhD Waël Hassan, PhD

The De-identification Maturity Model Authors: Khaled El Emam, PhD Waël Hassan, PhD A PRIVACY ANALYTICS WHITEPAPER The De-identification Maturity Model Authors: Khaled El Emam, PhD Waël Hassan, PhD De-identification Maturity Assessment Privacy Analytics has developed the De-identification

More information

DATA MINING - 1DL360

DATA MINING - 1DL360 DATA MINING - 1DL360 Fall 2013" An introductory class in data mining http://www.it.uu.se/edu/course/homepage/infoutv/per1ht13 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology,

More information

PUBLIC CONSULTATION ISSUED BY THE PERSONAL DATA PROTECTION COMMISSION

PUBLIC CONSULTATION ISSUED BY THE PERSONAL DATA PROTECTION COMMISSION PUBLIC CONSULTATION ISSUED BY THE PERSONAL DATA PROTECTION COMMISSION PROPOSED ADVISORY GUIDELINES ON THE PERSONAL DATA PROTECTION ACT FOR SELECTED TOPICS 05 FEBRUARY 2013 PART I: INTRODUCTION AND OVERVIEW...

More information

Robust De-anonymization of Large Sparse Datasets

Robust De-anonymization of Large Sparse Datasets Robust De-anonymization of Large Sparse Datasets Arvind Narayanan and Vitaly Shmatikov The University of Texas at Austin Abstract We present a new class of statistical deanonymization attacks against high-dimensional

More information

Fordham Intellectual Property, Media and Entertainment Law Journal

Fordham Intellectual Property, Media and Entertainment Law Journal Fordham Intellectual Property, Media and Entertainment Law Journal Volume 21, Issue 1 2011 Article 2 VOLUME XXI BOOK 1 The Deidentification Dilemma: A Legislative and Contractual Proposal Robert Gellman

More information

Please read the information below to learn more about our data collection policies and practices.

Please read the information below to learn more about our data collection policies and practices. WEBSITE PRIVACY POLICY This privacy policy is applicable to the website[s] and commerce URLs accessible at eink.com (individually and collectively the "Site"), which is operated by E Ink Corporation or

More information

Adaptive Privacy-Preserving Visualization Using Parallel Coordinates

Adaptive Privacy-Preserving Visualization Using Parallel Coordinates Adaptive Privacy-Preserving Visualization Using Parallel Coordinates Aritra Dasgupta and Robert Kosara Abstract Current information visualization techniques assume unrestricted access to data. However,

More information

The Need for Training in Big Data: Experiences and Case Studies

The Need for Training in Big Data: Experiences and Case Studies The Need for Training in Big Data: Experiences and Case Studies Guy Lebanon Amazon Background and Disclaimer All opinions are mine; other perspectives are legitimate. Based on my experience as a professor

More information

Dispelling the Myths Surrounding De-identification:

Dispelling the Myths Surrounding De-identification: Dispelling the Myths Surrounding De-identification: Anonymization Remains a Strong Tool for Protecting Privacy Ann Cavoukian, Ph.D. Information & Privacy Commissioner, Ontario, Canada Khaled El Emam, Ph.D.

More information

Healthcare data analytics. Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw

Healthcare data analytics. Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw Healthcare data analytics Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw Outline Data Science Enabling technologies Grand goals Issues Google flu trend Privacy Conclusion Analytics

More information