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
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Optimizing linear counting queries under differential privacy
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
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
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In this paper, we evaluate empirically the quality of statistical inference from differentially-private synthetic contingency tables. We compare three methods: histogram perturbation, the Dirichlet-Multinomial synthesizer and the Hardt-Ligett-McSherry algorithm. We consider a goodness-of-fit test for models suitable to the real data, and a model selection procedure. We find that the theoretical guarantees associated with these differentially-private datasets do not always translate well into guarantees about the statistical inference on the synthetic datasets.