Secure- multiparty Computation Protocol for Privacy Preserving Data Mining, M.Sc. Thesis Sharif University of Technology ; Amini, Morteza (Supervisor)
Abstract
Privacy preserving data mining helps organizations and companies not only to deal with privacy concerns of customers and regular limitations, but also to benefit from collaborative data mining. Utilizing cryptographic techniques and secure multiparty computation (SMC) are among widely employed approaches for preserving privacy in distributed data mining. The general purpose of secure multiparty computation protocols to compute specific functions on private inputs of parties in a collaborative manner and without revealing their private inputs. Providing rigorous security proof of secure multiparty computation makes it a good choice for privacy preservation, despite of its cryptographic...
Cataloging briefSecure- multiparty Computation Protocol for Privacy Preserving Data Mining, M.Sc. Thesis Sharif University of Technology ; Amini, Morteza (Supervisor)
Abstract
Privacy preserving data mining helps organizations and companies not only to deal with privacy concerns of customers and regular limitations, but also to benefit from collaborative data mining. Utilizing cryptographic techniques and secure multiparty computation (SMC) are among widely employed approaches for preserving privacy in distributed data mining. The general purpose of secure multiparty computation protocols to compute specific functions on private inputs of parties in a collaborative manner and without revealing their private inputs. Providing rigorous security proof of secure multiparty computation makes it a good choice for privacy preservation, despite of its cryptographic...
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