Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Differential privacy and robust statistics
Proceedings of the forty-first annual ACM symposium on Theory of computing
Pass-Efficient Algorithms for Learning Mixtures of Uniform Distributions
SIAM Journal on Computing
Differentially Private Empirical Risk Minimization
The Journal of Machine Learning Research
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
Proceedings of the 4th ACM workshop on Security and artificial intelligence
GUPT: privacy preserving data analysis made easy
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Functional mechanism: regression analysis under differential privacy
Proceedings of the VLDB Endowment
Testing the lipschitz property over product distributions with applications to data privacy
TCC'13 Proceedings of the 10th theory of cryptography conference on Theory of Cryptography
PrivGene: differentially private model fitting using genetic algorithms
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Differential privacy for functions and functional data
The Journal of Machine Learning Research
UMicS: from anonymized data to usable microdata
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A near-optimal algorithm for differentially-private principal components
The Journal of Machine Learning Research
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Consider an analyst who wants to release aggregate statistics about a data set containing sensitive information. Using differentially private algorithms guarantees that the released statistics reveal very little about any particular record in the data set. In this paper we study the asymptotic properties of differentially private algorithms for statistical inference. We show that for a large class of statistical estimators T and input distributions P, there is a differentially private estimator AT with the same asymptotic distribution as T. That is, the random variables AT(X) and T(X) converge in distribution when X consists of an i.i.d. sample from P of increasing size. This implies that AT(X) is essentially as good as the original statistic T(X) for statistical inference, for sufficiently large samples. Our technique applies to (almost) any pair T,P such that T is asymptotically normal on i.i.d. samples from P---in particular, to parametric maximum likelihood estimators and estimators for logistic and linear regression under standard regularity conditions. A consequence of our techniques is the existence of low-space streaming algorithms whose output converges to the same asymptotic distribution as a given estimator T (for the same class of estimators and input distributions as above).