Property testing and its connection to learning and approximation
Journal of the ACM (JACM)
Robust Characterizations of Polynomials withApplications to Program Testing
SIAM Journal on Computing
Testing the diameter of graphs
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Improved Testing Algorithms for Monotonicity
RANDOM-APPROX '99 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization Problems: Randomization, Approximation, and Combinatorial Algorithms and Techniques
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International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Distribution-Free Property-Testing
SIAM Journal on Computing
Composition attacks and auxiliary information in data privacy
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TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
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NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
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Proceedings of the 15th ACM SIGPLAN international conference on Functional programming
Privacy Violations Using Microtargeted Ads: A Case Study
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Privacy-preserving statistical estimation with optimal convergence rates
Proceedings of the forty-third annual ACM symposium on Theory of computing
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SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Testing and Reconstruction of Lipschitz Functions with Applications to Data Privacy
FOCS '11 Proceedings of the 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
ASIACRYPT'11 Proceedings of the 17th international conference on The Theory and Application of Cryptology and Information Security
GUPT: privacy preserving data analysis made easy
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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In the past few years, the focus of research in the area of statistical data privacy has been in designing algorithms for various problems which satisfy some rigorous notions of privacy. However, not much effort has gone into designing techniques to computationally verify if a given algorithm satisfies some predefined notion of privacy. In this work, we address the following question: Can we design algorithms which tests if a given algorithm satisfies some specific rigorous notion of privacy (e.g., differential privacy)? We design algorithms to test privacy guarantees of a given algorithm $\mathcal{A}$ when run on a dataset x containing potentially sensitive information about the individuals. More formally, we design a computationally efficient algorithm ${\cal T}_{priv}$ that verifies whether $\mathcal{A}$ satisfies differential privacy on typical datasets (DPTD) guarantee in time sublinear in the size of the domain of the datasets. DPTD, a similar notion to generalized differential privacy first proposed by [3], is a distributional relaxation of the popular notion of differential privacy [14]. To design algorithm ${\cal T}_{priv}$, we show a formal connection between the testing of privacy guarantee for an algorithm and the testing of the Lipschitz property of a related function. More specifically, we show that an efficient algorithm for testing of Lipschitz property can be used as a subroutine in ${\cal T}_{priv}$ that tests if an algorithm satisfies differential privacy on typical datasets. Apart from formalizing the connection between the testing of privacy guarantee and testing of the Lipschitz property, we generalize the work of [21] to the setting of property testing under product distribution. More precisely, we design an efficient Lipschitz tester for the case where the domain points are drawn from hypercube according to some fixed but unknown product distribution instead of the uniform distribution.