Privacy and Data Mining in the Electronic Society -- Overview

Xintao Wu

University of North Carolina at CharlotteAugust 20, 2012

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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

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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

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/

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

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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

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

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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

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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)?

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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

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Data miner vs. attacker

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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

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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

10 85 2

15 70 18

50

45

80

120

23

110

35

134

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= +

Y = X + E

7.334 3.759 0.099

4.199 7.537 7.939

9.199

6.208

9.048

8.447

7.313

5.692

3.678

1.939

6.318

17.334 88.759 2.099

19.199 77.537 25.939

59.199

51.208

89.048

128.447

30.313

115.692

38.678

135.939

21.318

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Additive Randomization (Z=X+Y)

50 | 40K | ... 30 | 70K | ... ...

...

Randomizer Randomizer

ReconstructDistribution

of Age

ReconstructDistributionof Salary

ClassificationAlgorithm

Model

65 | 20K | ... 25 | 60K | ... ...30

becomes 65

(30+35)

Alice’s age

Add random number to

Age

• R.Agrawal and R.Srikant SIGMOD 00

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Identity Theft

• SSN

### - ## - ####

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

Group no Sequential no

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Randomized Response ([ Stanley Warner; JASA 1965])

: Cheated in the exam : Didn’t cheat in the exam

Cheated in exam

Didn’t cheat

AAA

A

Randomization device

Do you belong to A? (p)

Do you belong to ?(1-p)A…

)1)(1( pp AA 12

ˆ

12

1ˆ

pp

pAW

1

“Yes” answer

“No” answer

As: Unbiased estimate of is: A

Procedure:

Purpose: Get the proportion( ) of population members that cheated in the exam.

A

…

Purpose

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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

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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].

attackersensitive link

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

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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