Communications of the ACM
Computational limitations on learning from examples
Journal of the ACM (JACM)
Universal one-way hash functions and their cryptographic applications
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
One-way functions are necessary and sufficient for secure signatures
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
A note on efficient zero-knowledge proofs and arguments (extended abstract)
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
Some optimal inapproximability results
Journal of the ACM (JACM)
SIAM Journal on Computing
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The complexity of properly learning simple concept classes
Journal of Computer and System Sciences
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Hardness of approximate two-level logic minimization and PAC learning with membership queries
Journal of Computer and System Sciences
Universal Arguments and their Applications
SIAM Journal on Computing
Interactive privacy via the median mechanism
Proceedings of the forty-second ACM symposium on Theory of computing
A firm foundation for private data analysis
Communications of the ACM
Boosting and Differential Privacy
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
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
Private data release via learning thresholds
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Iterative constructions and private data release
TCC'12 Proceedings of the 9th international conference on Theory of Cryptography
Faster algorithms for privately releasing marginals
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
A learning theory approach to noninteractive database privacy
Journal of the ACM (JACM)
Answering n{2+o(1)} counting queries with differential privacy is hard
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Faster private release of marginals on small databases
Proceedings of the 5th conference on Innovations in theoretical computer science
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Assuming the existence of one-way functions, we show that there is no polynomial-time, differentially private algorithm A that takes a database D ∈ ({0, 1}d)n and outputs a "synthetic database" D all of whose two-way marginals are approximately equal to those of D. (A two-way marginal is the fraction of database rows x ∈ {0, 1}d with a given pair of values in a given pair of columns). This answers a question of Barak et al. (PODS '07), who gave an algorithm running in time poly(n, 2d). Our proof combines a construction of hard-to-sanitize databases based on digital signatures (by Dwork et al., STOC '09) with encodings based on probabilistically checkable proofs. We also present both negative and positive results for generating "relaxed" synthetic data, where the fraction of rows in D satisfying a predicate c are estimated by applying c to each row of D and aggregating the results in some way.