Security problems on inference control for SUM, MAX, and MIN queries
Journal of the ACM (JACM)
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Matrix computations (3rd ed.)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Selective private function evaluation with applications to private statistics
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Sensitive Bayesian Network Parameter Learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Privacy-enhancing k-anonymization of customer data
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A new scheme on privacy-preserving data classification
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Improved Privacy-Preserving Bayesian Network Parameter Learning on Vertically Partitioned Data
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Foundations of Cryptography: Volume 1
Foundations of Cryptography: Volume 1
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Information Sciences: an International Journal
| Hi-index | 0.00 |
There have been two methods for privacy- preserving data mining: the perturbation approach and the cryptographic approach. The perturbation approach is typically very efficient, but it suffers from a tradeoff between accuracy and privacy. In contrast, the cryptographic approach usually maintains accuracy, but it is more expensive in computation and communication overhead. We propose a novel perturbation method, called guided perturbation. Specifically, we focus on a central problem of privacy-preserving data mining--the secure scalar product problem of vertically partitioned data, and give a solution based on guided perturbation, with good, provable privacy guarantee. Our solution achieves accuracy comparable to the cryptographic solutions, while keeping the efficiency of perturbation solutions. Our experimental results show that it can be more than one hundred times faster than a typical cryptographic solution.