On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
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
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
Privacy preserving regression modelling via distributed computation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
IEEE Transactions on Knowledge and Data Engineering
Secure multiparty computation of approximations
ACM Transactions on Algorithms (TALG)
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
The price of privacy and the limits of LP decoding
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
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
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Compressed and privacy-sensitive sparse regression
IEEE Transactions on Information Theory
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
Differentially Private Empirical Risk Minimization
The Journal of Machine Learning Research
SIAM Journal on Computing
A rigorous and customizable framework for privacy
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Non-interactive differential privacy: a survey
Proceedings of the First International Workshop on Open Data
Pufferfish: A framework for mathematical privacy definitions
ACM Transactions on Database Systems (TODS)
A near-optimal algorithm for differentially-private principal components
The Journal of Machine Learning Research
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This work studies formal utility and privacy guarantees for a simple multiplicative database transformation, where the data are compressed by a random linear or affine transformation, reducing the number of data records substantially, while preserving the number of original input variables. We provide an analysis framework inspired by a recent concept known as differential privacy. Our goal is to show that, despite the general difficulty of achieving the differential privacy guarantee, it is possible to publish synthetic data that are useful for a number of common statistical learning applications. This includes high dimensional sparse regression [24], principal component analysis (peA), and other statistical measures [16] based on the covariance of the initial data.