document: 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 .
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