Online query answering with differential privacy: a utility-driven approach using Bayesian inference

Details Online query answering with differential privacy: a utility-driven approach using Bayesian inference Online query answering with differential privacy: a utility-driven approach using Bayesian inference

2012-03-03

arxiv.org/abs/1203.0617v1

Computer Science

Databases

13 pages

Scientific paper

Data privacy issues frequently and increasingly arise for data sharing and data analysis tasks. In this paper, we study the problem of online query answering under the rigorous differential privacy model. The existing interactive mechanisms for differential privacy can only support a limited number of queries before the accumulated cost of privacy reaches a certain bound. This limitation has greatly hindered their applicability, especially in the scenario where multiple users legitimately need to pose a large number of queries. To minimize the privacy cost and extend the life span of a system, we propose a utility-driven mechanism for online query answering using Bayesian statistical inference. The key idea is to keep track of the query history and use Bayesian inference to answer a new query using previous query answers. The Bayesian inference algorithm provides both optimal point estimation and optimal interval estimation. We formally quantify the error of the inference result to determine if it satisfies the utility requirement. If not, the query is answered with the minimal privacy budget corresponding to its utility requirement. We show that our approach maintains lower privacy cost, answers more queries, achieves a longer system life span and provides more accurate estimations than traditional approach through extensive experiments on different real-life data sets.

Affiliated with

Also associated with

No associations

Rating

Online query answering with differential privacy: a utility-driven approach using Bayesian inference does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Online query answering with differential privacy: a utility-driven approach using Bayesian inference, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Online query answering with differential privacy: a utility-driven approach using Bayesian inference will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-345099