1. PRIVACY CRITERIA

2. Roadmap Privacy in Data mining Mobile privacy (k-e) – anonymity (c-k) – safety Privacy skyline

3. Privacy in data mining Random Perturbation (quantitative data)  Given value x, return value x + r, r is a random value from a distribution  Construct decision-tree classifier on perturbed data s.t. accuracy is comparable to classifiers of original data Randomized Response (categorical data)  Basic idea: disguise data by probabilistically changing the value of sensitive attribute to another value  Distribution of original data can be reconstructed using the disguised data

4. Roadmap Privacy in Data mining Mobile privacy (k-e) – anonymity (c-k) – safety Privacy skyline

5. Mobile privacy Spatial cloaking: Cloaked region  Contains location q and at least k-1 other user locations  Circular region of location q  Contains location q and number of dummy locations generated by client Transformation based matching  Transform region through Hilbert curves by using Hilbert keys Casper: user registers with (k, A min ) profile  k: user is k-anonymous  A min : minimum acceptable resolution of the cloaked spatial region

6. Roadmap Privacy in Data mining Mobile privacy (k-e) – anonymity (c-k) – safety Privacy skyline

7. (k-e) - anonymity Privacy protection for numerical sensitive attributes GOAL: group sensitive attribute values s.t.  No less than k distinct values  Range of group larger than threshold e Permutation-based technique to support aggregate queries  Constructing help table Aggregate Query Answering on Anonymized Tables @ ICDE2007

8. (k-e) - anonymity Original Table Table after Permutation

9. (k-e) - anonymity Table after Permutation Help Table

10. Roadmap Privacy in Data mining Mobile privacy (k-e) – anonymity (c-k) – safety Privacy skyline

11. (c-k) – safety Goal:  quantify background knowledge k of attacker  maximum disclosure w.r.t. k is less than threshold c Express background knowledge through a language Worst –Case Background Knowledge for Privacy –Preserving Data Publishing @ ICDE2007

12. (c-k) – safety Create buckets, where randomly permute sensitive attribute values within each bucket Original TableBucketized Table

13. (c-k) – safety Bound background knowledge i.e., attacker knows k basic implications Atom: t p [S] = s, s  S, p  Person  e.g. t Jack [Disease] = flu Basic implication:  For some m, n and A i, B i atoms  e.g. t Jack [Disease] = flu  t Charlie [Disease] = flu is the language consisting of conjunctions of k basic implications

14. (c-k) – safety Find bucketization B of original table s.t.  B is (c-k) – safe The maximum disclosure of B w.r.t is less than threshold c

15. Roadmap Privacy in Data mining Mobile privacy (k-e) – anonymity (c-k) – safety Privacy skyline

16. Original data transformed in Generalized or Bucketized data Quantify external knowledge through skyline for each sensitive value External knowledge for each individual  Having single sensitive value  Having multiple sensitive values Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge @ VLDB 2007

17. Privacy skyline Three types of knowledge (l, k, m) e.g.(2, 3, 1)  l: Knowledge about target individual t flue  Tom[S] and cancer  Tom[S] (obtained from Tom.s friend)  k: Knowledge about individuals (u 1,..u k ) other than t flue  Bob[S] and flue  Cary[S] and cancer  Frank[S] (obtained from another hospital)  m: Knowledge about the relationship between t and other individuals (v 1, …v m ) AIDS  Ann[S]  AIDS  Tom[S] (because Ann is Tom’s wife)

18. Privacy skyline Example: knowledge threshold (1, 5, 2) and confidence c=50% for sensitive value AIDS  Adversary knows l≤1 sensitive values that t does not have  Adversary knows sensitive values of k≤5 others  Adversary knows m≤2 members in t’s same-value family Adversary cannot predict individual t to have AIDS with confidence  50% when the above hold

19. Privacy skyline If transformed data D* is safe for (1, 5, 2) it is safe for any (l, k, m) with l≤1, k≤5, m≤2 i.e., the shaded region

20. Privacy skyline Skyline for set of incomparable points  {(1, 1, 5), (1, 3, 4), (1, 5, 2)}

21. Privacy skyline Given a skyline {(l 1, k 1, m 1, c 1 ), …,(l r, k r, m r, c r )} release candidate D* is safe for sensitive value  iff, for i =1 to r max {Pr(   t[S] | L t,  (l i, k i, m i ), D*)} < c i maximum probability of a sensitive value  to be for individual t w.r.t external knowledge and release candidate is below confidence threshold c i

22. Original TableGeneralize Table Bucketized Table