k-TTP: a new privacy model for large-scale distributed environments

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. Categories and Subject Descriptors H.2.8 [Database Managem...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Bobi Gilburd, Assaf Schuster, Ran Wolff
• Keywords: Data Mining, KDD 2004, Mining Keywords Privacy, Privacy-preserving Data Mining