Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Proceedings of the 16th ACM conference on Computer and communications security
Differential privacy under continual observation
Proceedings of the forty-second ACM symposium on Theory of computing
Optimizing linear counting queries under differential privacy
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
CoSaMP: iterative signal recovery from incomplete and inaccurate samples
Communications of the ACM
Private and continual release of statistics
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
Compressive mechanism: utilizing sparse representation in differential privacy
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Compressive mechanism: utilizing sparse representation in differential privacy
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Low-rank mechanism: optimizing batch queries under differential privacy
Proceedings of the VLDB Endowment
Functional mechanism: regression analysis under differential privacy
Proceedings of the VLDB Endowment
Distributed private heavy hitters
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Non-interactive differential privacy: a survey
Proceedings of the First International Workshop on Open Data
Optimal error of query sets under the differentially-private matrix mechanism
Proceedings of the 16th International Conference on Database Theory
Practical differential privacy via grouping and smoothing
Proceedings of the VLDB Endowment
Differential privacy based on importance weighting
Machine Learning
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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.