Slide 1

Differentially Private Data Release for Data Mining Benjamin C.M. Fung Concordia University Montreal, QC, Canada Noman Mohammed Concordia University Montreal, QC, Canada Rui Chen Concordia University Montreal, QC, Canada Philip S. Yu University of Illinois at Chicago, IL, USA

Slide 2

2 Outline Overview Differential privacy Related Work Our Algorithm Experimental results Conclusion 2

Slide 3

3 Overview 3 Privacy model Anonymization algorithm Data utility

Slide 4

4 Contributions Proposed an anonymization algorithm that provides differential privacy guarantee G eneralization-based algorithm for differentially private data release Proposed algorithm can handle both categorical and numerical attributes Preserves information for classification analysis 4

Slide 5

5 Outline Overview Differential privacy Related Work Our Algorithm Experimental results Conclusion 5

Slide 6

6 Differential Privacy [DMNS06] 6 A non-interactive privacy mechanism A gives -differential privacy if for all neighbour D and D, and for any possible sanitized database D* Pr A [A(D) = D*] exp() Pr A [A(D) = D*] DD D and D are neighbors if they differ on at most one record

Slide 7

7 Laplace Mechanism [DMNS06] 7 For example, for a single counting query Q over a dataset D, returning Q(D) + Laplace(1/) maintains -differential privacy. f = max D,D ||f(D) f(D)|| 1 For a counting query f: f =1

Slide 8

8 Given a utility function u : ( D T ) R for a database instance D, the mechanism A, A(D, u) = return t with probability proportional to exp(u(D, t)/2 u) gives -differential privacy. Exponential Mechanism [MT07] 8

Slide 9

9 Composition properties 9 Sequential composition i i differential privacy Parallel composition max( i )differential privacy

Slide 10

10 Outline Overview Differential privacy Related Work Our Algorithm Experimental results Conclusion 10

Slide 11

11 Two Frameworks Interactive: Multiple questions asked/answered adaptively Anonymizer

Slide 12

12 Two Frameworks Interactive: Multiple questions asked/answered adaptively Anonymizer Non-interactive: Data is anonymized and released

Slide 13

13 Related Work 13 A. Blum, C. Dwork, F. McSherry, and K. Nissim. Practical privacy: The SuLQ framework. In PODS, 2005. A. Friedman and A. Schuster. Data mining with differential privacy. In SIGKDD, 2010. Is it possible to release data for classification analysis ?

Slide 14

14 Why Non-interactive framework ? 14 Disadvantages of interactive approach: Database can answer a limited number of queries Big problem if there are many data miners Provide less flexibility to perform data analysis

Slide 15

15 Non-interactive Framework 0 + Lap(1/ ) 15

Slide 16

16 For high-dimensional data, noise is too big 0 + Lap(1/ ) 16 Non-interactive Framework

Slide 17

17 Non-interactive Framework

Slide 18

18 Outline Overview Differential privacy Related Work Our Algorithm Experimental results Conclusion 18

Slide 19

19 JobAgeClassCount Any_Job[18-65)4Y4N8 Artist[18-65)2Y2N4 Professional[18-65)2Y2N4 Age [18-65) [18-40)[40-65) Artist[18-40)2Y2N4Artist[40-65)0Y0N0 Anonymization Algorithm [18-30)[30-40) 19 Professional[18-40)2Y1N3Professional[40-65)0Y1N1 Job Any_Job ProfessionalArtist EngineerLawyerDancerWriter

Slide 20

20 Candidate Selection we favor the specialization with maximum Score value First utility function: u = Second utility function: u = 1 20

Slide 21

21 Split Value The split value of a categorical attribute is determined according to the taxonomy tree of the attribute How to determine the split value for numerical attribute ? 21

Slide 22

22 Split Value The split value of a categorical attribute is determined according to the taxonomy tree of the attribute How to determine the split value for numerical attribute ? AgeClass 60 Y 30 N 25 Y 40 N 25 Y 40 N 45 N 25 Y 1865 40 30 60 2545 22

Slide 23

23 Anonymization Algorithm O(A pr x|D|log|D|) O(|candidates|) O(|D|) O(|D|log|D|) O(1) 23

Slide 24

24 Anonymization Algorithm O(A pr x|D|log|D|) O(|candidates|) O(|D|) O(|D|log|D|) O(1) O((A pr +h)x|D|log|D|) 24

Slide 25

25 Outline Overview Differential privacy Related Work Our Algorithm Experimental results Conclusion 25

Slide 26

26 Experimental Evaluation Adult: is a Census data (from UCI repository) 6 continuous attributes. 8 categorical attributes. 45,222 census records 26

Slide 27

27 Data Utility for Max 27

Slide 28

28 Data Utility for InfoGain 28

Slide 29

29 Comparison 29

Slide 30

30 Scalability 30

Slide 31

31 Outline Overview Differential privacy Related Work Our Algorithm Experimental results Conclusion 31

Slide 32

32 Differentially Private Data Release Generalization-based differentially private algorithm Provides better utility than existing techniques Conclusions 32

Slide 33

33 Q&A Thank You Very Much 33