Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output

01/06/2014
by   Chi Jin, et al.
0

We consider accurately answering smooth queries while preserving differential privacy. A query is said to be K-smooth if it is specified by a function defined on [-1,1]^d whose partial derivatives up to order K are all bounded. We develop an ϵ-differentially private mechanism for the class of K-smooth queries. The major advantage of the algorithm is that it outputs a synthetic database. In real applications, a synthetic database output is appealing. Our mechanism achieves an accuracy of O (n^-K/2d+K/ϵ ), and runs in polynomial time. We also generalize the mechanism to preserve (ϵ, δ)-differential privacy with slightly improved accuracy. Extensive experiments on benchmark datasets demonstrate that the mechanisms have good accuracy and are efficient.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset