Multi party computations: past and present
PODC '97 Proceedings of the sixteenth annual ACM symposium on Principles of distributed computing
Efficient private bidding and auctions with an oblivious third party
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
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
A Secure Protocol for Computing Dot-Products in Clustered and Distributed Environments
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
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The proliferation of the network has opened up great opportunities for cooperative computation. But privacy concerns often prevent different parties from sharing their data in order to do cooperative computation tasks. Secure multi-party computation deals with the privacy concern in cooperative computation while ensuring correctness of the computation and that no more information is revealed to a participant in the computation than can be inferred from that participant's input and output [10]. This paper addresses the problem of privacy preserving collision detection of two circles for the first time, which is an important problem in privacy preserving computational geometry. Four protocols are presented in this paper to solve the problem, and their correctness and security are also analyzed. The experimental results illustrate that our method for detecting collision of two moving circles is very effective.