@inproceedings{012586e7924c4ad5a212e37c7bedda57,

title = "k-TTP: A new privacy model for large-scale distributed environments",

abstract = "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.",

keywords = "Association rule mining, Data mining, Distributed data mining, Privacy, Privacy-preserving data mining, Security",

author = "Bobi Gilburd and Assaf Schuster and Ran Wolff",

year = "2004",

language = "English",

isbn = "1581138881",

series = "KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

pages = "563--568",

editor = "R. Kohavi and J. Gehrke and W. DuMouchel and J. Ghosh",

booktitle = "KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

note = "KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; Conference date: 22-08-2004 Through 25-08-2004",

}