1. Differential Privacy Xintao Wu Oct 31, 2012

2. Sanitization approaches Input perturbation –Add noise to data –Generalize data Summary statistics –Means, variances –Marginal totals –Model parameters Output perturbation –Add noise to summary statistics

3. Blending/hiding into a crowd K-anonymity based approaches Adversary may have various background knowledge to breach privacy Privacy models often assume “the adversary’s background knowledge is given”

4. Classic intuition for privacy Privacy means that anything can be learned about a respondent from the statistical database can be learned without access to the database. Security of encryption –Anything about the plaintext that can be learned from a ciphertext can be learned without the ciphertext. Prior and posterior views about an individual should not change much

5. Motivation Publicly release statistical information about a dataset without compromising the privacy of any individual

6. Requirement Anything that can be learned about a respondent from a statistical database should be learnable without access to the database Reduce the knowledge gain of joining the database Require that the probability distribution on the public results is essentially the same independent of whether any individual opts in to, or opts out of the dataset

7. Definition

8. Sensitivity function Captures how great a difference must be hidden by the additive noise

9. LAP distribution noise

10. Guassian noise

11. Adding LAP noise

12. Proof sketch

13. Delta_f=1, epsilon varies

14. Delta_f=1 epsilon=0.01

15. Delta_f=1 epsilon=0.1

16. Delta_f=1 epsilon=1

17. Delta_f=1 epsilon=2

18. Delta_f=1 epsilon=10

19. Delta_f=2, epsilon varies

20. Delta_f=3, epsilon varies

21. Delta_f=10000, epsilon varies

22. Composition Sequential composition Parallel composition --for disjoint sets, the ultimate privacy guarantee depends only on the worst of the guarantees of each analysis, not the sum.

23. Example Let us assume a table with 1000 customers and each record has attributes: name, gender, city, cancer, salary. –For attribute city, we assume the domain size is 10; –for attribute cancer, we only record Yes or No for each customer; – for attribute salary, the domain range is 0-10k. –The privacy threshold \epsilon is a constant 0.1 set by data owner. For one single query “How many customers got cancer?” The adversary is allowed to ask three times of the query shown the above.

24. Example (continued) “How many customers got cancer in each city?” For one single query “What is the sum of salaries across all customers?”

25. Type of computing (query) some are very sensitive, others are not single query vs. query sequence query on disjoint sets or not outcome expected: number vs. arbitrary interactive vs. not interactive

26. Sensitivity Global sensitivity Local sensitivity Smooth sensitivity

27. Different areas of DP PINQ DM with DP Optimizing linear counting queries under differential privacy. -Matrix mechanism for answering a workload of predicate counting queries

28. PPDM interface--PINQ A programmable privacy preserving layer Add calibrated noise to each query Need to assign privacy cost budget

29. Data Mining with DP Previous study—privacy preserving interface ensures everything about DP Problems—inferior results if the interface is utilized simply during data mining Solution—consider both together DP ID3 —noisy count —evaluate all attributes in one exponential mechanism query using entire budget instead of splitting budget among multiple

30. DP in Social Networks Page 97-120 of pakdd11 tutorialpakdd11 tutorial