Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Convergence assessment techniques for Markov chain Monte Carlo
Statistics and Computing
Limiting privacy breaches in privacy preserving data mining
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
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
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Smooth sensitivity and sampling in private data analysis
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
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Differential privacy and robust statistics
Proceedings of the forty-first annual ACM symposium on Theory of computing
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy-Preserving Singular Value Decomposition
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Comparison of methodologies to assess the convergence of Markov chain Monte Carlo methods
Computational Statistics & Data Analysis
Accurate Estimation of the Degree Distribution of Private Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Differential privacy with compression
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Privacy-preserving support vector machine classification
International Journal of Intelligent Information and Database Systems
Closeness: A New Privacy Measure for Data Publishing
IEEE Transactions on Knowledge and Data Engineering
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An agent-based approach to care in independent living
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Privacy-preserving statistical estimation with optimal convergence rates
Proceedings of the forty-third annual ACM symposium on Theory of computing
Differentially private data release for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Differentially Private Empirical Risk Minimization
The Journal of Machine Learning Research
When random sampling preserves privacy
CRYPTO'06 Proceedings of the 26th annual international conference on Advances in Cryptology
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
Beating randomized response on incoherent matrices
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
The capacity of the arbitrarily varying channel revisited: positivity, constraints
IEEE Transactions on Information Theory
Capacity of the Gaussian arbitrarily varying channel
IEEE Transactions on Information Theory
On significance of the least significant bits for differential privacy
Proceedings of the 2012 ACM conference on Computer and communications security
The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy
FOCS '12 Proceedings of the 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science
Beyond worst-case analysis in private singular vector computation
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Hi-index | 0.00 |
The principal components analysis (PCA) algorithm is a standard tool for identifying good low-dimensional approximations to high-dimensional data. Many data sets of interest contain private or sensitive information about individuals. Algorithms which operate on such data should be sensitive to the privacy risks in publishing their outputs. Differential privacy is a framework for developing tradeoffs between privacy and the utility of these outputs. In this paper we investigate the theory and empirical performance of differentially private approximations to PCA and propose a new method which explicitly optimizes the utility of the output. We show that the sample complexity of the proposed method differs from the existing procedure in the scaling with the data dimension, and that our method is nearly optimal in terms of this scaling. We furthermore illustrate our results, showing that on real data there is a large performance gap between the existing method and our method.