Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
New Efficient Attacks on Statistical Disclosure Control Mechanisms
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
Releasing search queries and clicks privately
Proceedings of the 18th international conference on World wide web
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Accurate Estimation of the Degree Distribution of Private Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
Differential Privacy via Wavelet Transforms
IEEE Transactions on Knowledge and Data Engineering
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
Adaptive differentially private histogram of low-dimensional data
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Understanding hierarchical methods for differentially private histograms
Proceedings of the VLDB Endowment
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In this paper we consider the problem of differentially private data publishing. In particular, we consider the scenario in which a trusted curator gathers sensitive information from a large number of respondents, creates a relational dataset where each tuple corresponds to one entity, such as an individual, a household, or an organization, and then publishes a privacy-preserving (i.e., sanitized or anonymized) version of the dataset. This has been referred to as the "non-interactive" mode of private data analysis, as opposed to the "interactive" mode, where the data curator provides an interface through which users may pose queries about the data, and get (possibly noisy) answers.