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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) 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 2 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 3 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/ 4 Source: http://www.privacyinternational.org/issues/foia/foia-laws.jpg 5 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 Two Approaches • 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 11 Data miner vs. attacker 12 Scope ssn name zip race … age Sex Bal income … IntP 1 28223 Asian … 20 M 10k 85k … 2k 2 28223 Asian … 30 F 15k 70k … 18k 3 28262 Black … 20 M 50k 120k … 35k 4 28261 White … 26 M 45k 23k … 134k . . . … . . . . … . N 28223 Asian … 20 M 80k 110k … 15k 69% unique on zip and birth date 87% with zip, birth date and gender. k-anonymity, L-diversity SDC etc. Generalization/randomization 13 Additive Noise Randomization Example Bal income … IntP 1 10k 85k … 2k 2 15k 70k … 18k 3 50k 120k … 35k 4 45k 23k … 134k . . . … . N 80k 110k … 15k 17.334 88.759 2.099 19.199 77.537 25.939 59.199 128.447 38.678 51.208 30.313 135.939 89.048 115.692 21.318 Y = = 7.334 3.759 0.099 4.199 7.537 7.939 35 9.199 8.447 3.678 23 134 6.208 7.313 1.939 110 15 9.048 5.692 6.318 10 85 2 15 70 18 50 120 45 80 X 14 + + E Additive Randomization (Z=X+Y) • R.Agrawal and R.Srikant SIGMOD 00 Alice’s age Add random number to Age 30 becomes 65 (30+35) 30 | 70K | ... 50 | 40K | ... Randomizer Randomizer 65 | 20K | ... 25 | 60K | ... Reconstruct Distribution of Age Reconstruct Distribution of Salary Classification Algorithm ... ... ... Model 15 Identity Theft • SSN ### - ## - #### Determined by zip code Group no Sequential no 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]) A : Cheated in the exam A : Didn’t cheat in the exam A Purpose Purpose: Get the proportion( A) of population members that cheated in the exam. Procedure: Cheated in exam A “Yes” answer Didn’t cheat Randomization device Do you belong to A? (p) … … Do you belong to A?(1-p) 1 “No” answer As: A p (1 A )(1 p) Unbiased estimate of ˆ AW 17 A p 1 ˆ 2 p 1 2 p 1 is: Linked data ssn name zip race … age Sex Bal income … IntP 1 28223 Asian … 20 M 10k 85k … 2k 2 28223 Asian … 30 F 15k 70k … 18k 3 28262 Black … 20 M 50k 120k … 35k 4 28261 White … 26 M 45k 23k … 134k . . . … . . . . … . N 28223 Asian … 20 M 80k 110k … 15k sensitive links 18 Privacy issues in Social Network • 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]. sensitive link attacker 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 ecommerce, 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 21