Computer Science – Data Structures and Algorithms
Scientific paper
2011-09-10
Computer Science
Data Structures and Algorithms
Full Version. Extended Abstract appeared in STOC 2008
Scientific paper
In this paper we demonstrate that, ignoring computational constraints, it is possible to privately release synthetic databases that are useful for large classes of queries -- much larger in size than the database itself. Specifically, we give a mechanism that privately releases synthetic data for a class of queries over a discrete domain with error that grows as a function of the size of the smallest net approximately representing the answers to that class of queries. We show that this in particular implies a mechanism for counting queries that gives error guarantees that grow only with the VC-dimension of the class of queries, which itself grows only logarithmically with the size of the query class. We also show that it is not possible to privately release even simple classes of queries (such as intervals and their generalizations) over continuous domains. Despite this, we give a privacy-preserving polynomial time algorithm that releases information useful for all halfspace queries, given a slight relaxation of the utility guarantee. This algorithm does not release synthetic data, but instead another data structure capable of representing an answer for each query. We also give an efficient algorithm for releasing synthetic data for the class of interval queries and axis-aligned rectangles of constant dimension. Finally, inspired by learning theory, we introduce a new notion of data privacy, which we call distributional privacy, and show that it is strictly stronger than the prevailing privacy notion, differential privacy.
Blum Avrim
Ligett Katrina
Roth Aaron
No associations
LandOfFree
A Learning Theory Approach to Non-Interactive Database Privacy 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 A Learning Theory Approach to Non-Interactive Database Privacy, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Learning Theory Approach to Non-Interactive Database Privacy will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-38647