A random polynomial-time algorithm for approximating the volume of convex bodies
Journal of the ACM (JACM)
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Universally utility-maximizing privacy mechanisms
Proceedings of the forty-first annual ACM symposium on Theory of computing
On the complexity of differentially private data release: efficient algorithms and hardness results
Proceedings of the forty-first annual ACM symposium on Theory of computing
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
On the geometry of differential privacy
Proceedings of the forty-second ACM symposium on Theory of computing
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Differential privacy and the fat-shattering dimension of linear queries
APPROX/RANDOM'10 Proceedings of the 13th international conference on Approximation, and 14 the International conference on Randomization, and combinatorial optimization: algorithms and techniques
Differentially private data release through multidimensional partitioning
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
PCPs and the hardness of generating private synthetic data
TCC'11 Proceedings of the 8th conference on Theory of cryptography
iReduct: differential privacy with reduced relative errors
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Privately releasing conjunctions and the statistical query barrier
Proceedings of the forty-third annual ACM symposium on Theory of computing
Differentially private data release for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Private data release via learning thresholds
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
An adaptive mechanism for accurate query answering under differential privacy
Proceedings of the VLDB Endowment
Unconditional differentially private mechanisms for linear queries
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Optimal private halfspace counting via discrepancy
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Iterative constructions and private data release
TCC'12 Proceedings of the 9th international conference on Theory of Cryptography
Integrating historical noisy answers for improving data utility under differential privacy
Proceedings of the 15th International Conference on Extending Database Technology
The application of differential privacy to health data
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
Secure distributed framework for achieving ε-differential privacy
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Faster algorithms for privately releasing marginals
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Optimal error of query sets under the differentially-private matrix mechanism
Proceedings of the 16th International Conference on Database Theory
A learning theory approach to noninteractive database privacy
Journal of the ACM (JACM)
Publishing trajectories with differential privacy guarantees
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Privacy-preserving data exploration in genome-wide association studies
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Practical differential privacy via grouping and smoothing
Proceedings of the VLDB Endowment
How robust are linear sketches to adaptive inputs?
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Differential privacy for the analyst via private equilibrium computation
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
The geometry of differential privacy: the sparse and approximate cases
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Answering n{2+o(1)} counting queries with differential privacy is hard
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Geo-indistinguishability: differential privacy for location-based systems
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
A general framework for privacy preserving data publishing
Knowledge-Based Systems
Faster private release of marginals on small databases
Proceedings of the 5th conference on Innovations in theoretical computer science
Mechanism design in large games: incentives and privacy
Proceedings of the 5th conference on Innovations in theoretical computer science
Distributed and Parallel Databases
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We define a new interactive differentially private mechanism --- the median mechanism --- for answering arbitrary predicate queries that arrive online. Given fixed accuracy and privacy constraints, this mechanism can answer exponentially more queries than the previously best known interactive privacy mechanism (the Laplace mechanism, which independently perturbs each query result). With respect to the number of queries, our guarantee is close to the best possible, even for non-interactive privacy mechanisms. Conceptually, the median mechanism is the first privacy mechanism capable of identifying and exploiting correlations among queries in an interactive setting. We also give an efficient implementation of the median mechanism, with running time polynomial in the number of queries, the database size, and the domain size. This efficient implementation guarantees privacy for all input databases, and accurate query results for almost all input distributions. The dependence of the privacy on the number of queries in this mechanism improves over that of the best previously known efficient mechanism by a super-polynomial factor, even in the non-interactive setting.