Compressive mechanism
- 17 October 2011
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 177-182
- https://doi.org/10.1145/2046556.2046581
Abstract
Differential privacy provides the first theoretical foundation with provable privacy guarantee against adversaries with arbitrary prior knowledge. The main idea to achieve differential privacy is to inject random noise into statistical query results. Besides correctness, the most important goal in the design of a differentially private mechanism is to reduce the effect of random noise, ensuring that the noisy results can still be useful. This paper proposes the compressive mechanism, a novel solution on the basis of state-of-the-art compression technique, called compressive sensing. Compressive sensing is a decent theoretical tool for compact synopsis construction, using random projections. In this paper, we show that the amount of noise is significantly reduced from O(n) to O(log(n)), when the noise insertion procedure is carried on the synopsis samples instead of the original database. As an extension, we also apply the proposed compressive mechanism to solve the problem of continual release of statistical results. Extensive experiments using real datasets justify our accuracy claims.Keywords
This publication has 16 references indexed in Scilit:
- Compressive mechanismPublished by Association for Computing Machinery (ACM) ,2011
- CoSaMPCommunications of the ACM, 2010
- Boosting the accuracy of differentially private histograms through consistencyProceedings of the VLDB Endowment, 2010
- Differential privacy under continual observationPublished by Association for Computing Machinery (ACM) ,2010
- Robust De-anonymization of Large Sparse Datasets2008 IEEE Symposium on Security and Privacy (SP 2008), 2008
- Compressed sensingIEEE Transactions on Information Theory, 2006
- Stable signal recovery from incomplete and inaccurate measurementsCommunications on Pure and Applied Mathematics, 2006
- Differential PrivacyLecture Notes in Computer Science, 2006
- Calibrating Noise to Sensitivity in Private Data AnalysisLecture Notes in Computer Science, 2006
- Decoding by Linear ProgrammingIEEE Transactions on Information Theory, 2005