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Privacy and Data Mining in the Electronic Society -- Overview

Privacy and Data Mining in the Electronic Society -- Overview. Xintao Wu University of North Carolina at Charlotte August 20, 2012. Privacy Case. Nydia Velázquez  (1982)

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Privacy and Data Mining in the Electronic Society -- Overview

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  1. Privacy and Data Mining in the Electronic Society -- Overview Xintao Wu University of North Carolina at Charlotte August 20, 2012

  2. Privacy Case • Nydia Velázquez  (1982) Three weeks after Nydia Velázquez won the New York Democratic Party's nomination to serve in the U.S. House of Representatives, somebody at St. Claire Hospital in New York faxed Velázquez's medical records to the New York Post. The records detailed the care that Velázquez had received at the hospital after a suicide attempt--an attempt that had happened several years before the election. Database Nation: The Death of Privacy in the 21st Century,Simson Garfinkel, Jan 2000, 1-56592-653-6

  3. Privacy Case • AOL's publication of the search histories of more than 650,000 of its users has yielded more than just one of the year's bigger privacy scandals.(Aug 6, 2006) That database does not include names or user identities. Instead, it lists only a unique ID number for each user. AOL user 710794 • an overweight golfer, owner of a 1986 Porsche 944 and 1998 Cadillac SLS, and a fan of the University of Tennessee Volunteers Men's Basketball team. • interested in the Cherokee County School District in Canton, Ga., and has looked up the Suwanee Sports Academy in Suwanee, Ga., which caters to local youth, and the Youth Basketball of America's Georgia affiliate. • regularly searches for "lolitas," a term commonly used to describe photographs and videos of minors who are nude or engaged in sexual acts. AOL's disturbing glimpse into users' lives By Declan McCullagh , CNET News.com, August 7, 2006, 8:05 PM PDT

  4. NetFlix Prize • An open competition for the best collaborative filtering algorithm to predict user ratings for films. • On Sept 21 2009, the grand prize $1M was given to BellKor’s Pragmatic Chaos team which bested Netflix’s own algorithm for predicting ratings by 10.06%. • Wiki http://en.wikipedia.org/wiki/Netflix_Prize • NetFlix cancels contest after privacy lawsuit on March 12, 2010. • http://www.wired.com/threatlevel/2010/03/netflix-cancels-contest/

  5. Source: http://www.privacyinternational.org/issues/foia/foia-laws.jpg

  6. National Laws • USA • HIPAA for health care • Passed August 21, 96 • lowest bar and the States are welcome to enact more stringent rules • California State Bill 1386 • Grann-Leach-Bliley Act of 1999 for financial institutions • COPPA for childern’s online privacy • etc. • Canada • PIPEDA 2000 • Personal Information Protection and Electronic Documents Act • Effective from Jan 2004 • European Union (Directive 94/46/EC) • Passed by European Parliament Oct 95 and Effective from Oct 98. • Provides guidelines for member state legislation • Forbids sharing data with states that do not protect privacy

  7. Privacy & Breaches of Privacy • Various definitions of privacy • http://www.privacy.org • http://en.wikipedia.org/wiki/Privacy • Context dependent definitions: physical privacy, internet privacy, medical privacy, genetics, political privacy, surveillance. • An individual right • The claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others. – Alan Westin • Expansion of government and company databases & growing use of web and mobile devices lead to increase of collection, analysis and disclosure of sensitive information. • Location based services need user’s position and preference

  8. Privacy vs. Confidentiality • Privacy is the right to keep one’s personal information out of the public view • Confidentiality is the dissemination without public identification • Disclosure • Identity disclosure = when a specific person’s record can be found in a released file. • Attribute disclosure = when sensitive information about a specific person is revealed through the released file, sometimes with additional knowledge. • Inferential disclosure = if from the released data one can determine the value of some characteristic of an individual more accurately than otherwise would have been possible

  9. Mining vs. Privacy • Data mining • The goal of data mining is summary results (e.g., classification, cluster, association rules etc.) from the data (distribution) • Individual Privacy • Individual values in database must not be disclosed, or at least no close estimation can be got by attackers • Contractual limitations: privacy policies, corporate agreements • Privacy Preserving Data Mining (PPDM) • How to transform data such that • we can build a good data mining model (data utility) • while preserving privacy at the record level (privacy)?

  10. Distributed Suitable for multi-party platforms Secure multi-party computation Tolerated disclosure: computationally private Generalization/randomization/transformation Perturb data to protect privacy of individual records. Preserve intrinsic distributions necessary for modeling. Tolerated disclosure: statistically private Two Approaches

  11. Data miner vs. attacker

  12. Scope 69% unique on zip and birth date 87% with zip, birth date and gender. k-anonymity, L-diversity SDC etc. Generalization/randomization

  13. Y X E = + Additive Noise Randomization Example = +

  14. Additive Randomization (Z=X+Y) • R.Agrawal and R.Srikant SIGMOD00 Alice’s age 30 | 70K | ... 50 | 40K | ... ... Add random number to Age Randomizer Randomizer 65 | 20K | ... 25 | 60K | ... ... 30 becomes 65 (30+35) Reconstruct Distribution of Age Reconstruct Distribution of Salary ... Classification Algorithm Model

  15. Identity Theft • SSN ### - ## - #### Sequential no Group no Determined by zip code https://secure.ssa.gov/apps10/poms.nsf/lnx/0100201030 Facebook study http://www.heinz.cmu.edu/~acquisti/papers/ privacy-facebook-gross-acquisti.pdf

  16. Randomized Response ([ Stanley Warner; JASA 1965]) : Cheated in the exam : Didn’t cheat in the exam Cheated in exam Purpose Purpose: Get the proportion( ) of population members that cheated in the exam. • Procedure: “Yes” answer Didn’t cheat Randomization device Do you belong to A? (p) Do you belong to ?(1-p) … … “No” answer As: Unbiased estimate of is:

  17. Linked data sensitive links

  18. Privacy issues in Social Network sensitive link attacker Social network contains much private relation information; Anonymization is not enough for protecting the privacy. Subgraph attacks [Backstrom et al., WWW07, Hay et al., 07].

  19. Other issues • Statistical disclosure limitation methods for tabular/microdata • Secure multi-party computation protocols and tools • Privacy issues in various application areas such as e-commerce, healthcare, finance, and RFID

  20. Tutorials on PPDM • Privacy in data system, Rakesh Agrawal, PODS03 • Privacy preserving data mining, Chris Clifton, PKDD02, KDD03 • Preserving privacy in database systems, Johann-Chrostoph Freytag, WAIM06 • Models and methods for privacy preserving data publishing and analysis, Johannes Gehrke, ICDM05, ICDE06, KDD06 • Cryptographic techniques in privacy preserving data mining, Helger Lipmaa, PKDD06 • Randomization based privacy preserving data mining, Xintao Wu, PKDD06 & WAIM06

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