Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 23, Issue 8, Pages 1200-1214Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2010.247
Keywords
Privacy-preserving data publishing; differential privacy; wavelets
Categories
Funding
- Nanyang Technological University [M58020016, RG 35/09]
- New York State Foundation for Science, Technology, and Innovation [C050061]
- National Science Foundation [0627680]
- Research Council of Norway
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [0627680] Funding Source: National Science Foundation
Ask authors/readers for more resources
Privacy-preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, epsilon-differential privacy provides the strongest privacy guarantee. Existing data publishing methods that achieve epsilon-differential privacy, however, offer little data utility. In particular, if the output data set is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. In this paper, we develop a data publishing technique that ensures epsilon-differential privacy while providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data, we show the effectiveness and efficiency of our solution.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available