STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Association Rule Mining in Peer-to-Peer Systems
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Leveraging the "Multi" in secure multi-party computation
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
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
Privacy-Preserving Frequent Pattern Mining across Private Databases
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Client-side web mining for community formation in peer-to-peer environments
ACM SIGKDD Explorations Newsletter
Providing k-anonymity in data mining
The VLDB Journal — The International Journal on Very Large Data Bases
An efficient protocol for private and accurate mining of support counts
Pattern Recognition Letters
Information Sciences: an International Journal
Practical issues on privacy-preserving health data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Learning latent variable models from distributed and abstracted data
Information Sciences: an International Journal
Towards privacy-preserving computing on distributed electronic health record data
Proceedings of the 2013 Middleware Doctoral Symposium
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Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a general construction of such protocols for any function. Protocols obtained in this way are, however, inefficient, and thus, practically speaking, useless when a large number of participants are involved.The contribution of this paper is to define a new privacy model -- k-privacy -- by means of an innovative, yet natural generalization of the accepted trusted third party model. This allows implementing cryptographically secure efficient primitives for real-world large-scale distributed systems.As an example for the usefulness of the proposed model, we employ k-privacy to introduce a technique for obtaining knowledge -- by way of an association-rule mining algorithm -- from large-scale Data Grids, while ensuring that the privacy is cryptographically secure.