STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Secure Multi-party Computational Geometry
WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A practical approach to solve Secure Multi-party Computation problems
Proceedings of the 2002 workshop on New security paradigms
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Toward empirical aspects of secure scalar product
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Information theoretical analysis of two-party secret computation
DBSEC'06 Proceedings of the 20th IFIP WG 11.3 working conference on Data and Applications Security
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Privacy protection has become one of the most important issues in the information era. Thus, many protocols have been developed to achieve the goal of cooperatively accomplishing a computational task without revealing the participants' private data. Practical protocols, however, do not guarantee perfect privacy protection, as some degree of privacy leakage is allowed during the computation process for the sake of efficient resource consumption, e.g., the number of random bits required and the computation time. Although there are metrics for measuring the amount of resource consumption, as far as we know, there are no effective metrics that measure the degree of privacy leakage. Without such metrics, however, it is difficult to compare protocols fairly. In this paper, we propose a framework based on linear algebra and information theory to measure the amount of privacy leakage in protocols. This framework can be used to analyze protocols that satisfy certain algebraic properties. We use it to analyze three two-party scalar product protocols. The framework might also be extendable to the analysis of other protocols.