Differential Privacy Sample Clauses

Differential Privacy. Differential privacy has emerged as one of the strongest privacy definitions for statistical data release. It guarantees that if an adversary knows complete information of all the tuples in D except one, the output of a differentially private randomized algorithm should not give the adversary too much additional information about the remaining tuples. We say datasets D and Dj differing in only one tuple if we can obtain Dj by removing or adding only one tuple from D. A formal definition of differential privacy is given as follows:

Differential Privacy. Intuitively, a randomized mechanism A is differ- entially private if its outcome is not significantly affected by the removal or addi- tion of any record. s-differential privacy is formally defined as Pr[A(D) ∈ O] ≤ esPr[A(Dj) ∈ O], where O is any arbitrary set of possible outputs of A, D and Dj are two neighbouring datasets differing in at most one record (i.e. D can be obtained from Dj by adding or removing at most one record). In our problem definition, an adversary should learn approximately the same information about any individual user given D˜ , irrespective of its presence or absence in D, and one individual can be present in up to N snapshots in D. Two series of dynamic datasets D and Dˆ are user-level neighbors if one can be obtained by adding or removing one individual (including all its occurrences in the snapshots) from the other. Then user-level s-differential privacy is defined as below.