Two methods for privacy preserving data mining with malicious participants
Information Sciences: an International Journal
Transforming semi-honest protocols to ensure accountability
Data & Knowledge Engineering
Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
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Secure multi-party computation (SMC) balances the use and confidentiality of distributed data. This is especially important for privacy-preserving data mining (PPDM). Most secure multi-party computation protocols are only proven secure under the semi-honest model, providing insufficient security for many PPDM applications. SMC protocols under the malicious adversary model generally have impractically high complexities for PPDM. We propose an accountable computing (AC) framework that enables liability for privacy compromise to be assigned to the responsible party without the complexity and cost of an SMC-protocol under the malicious model. We show how to transform a circuitbased semi-honest two-party protocol into a simple and efficient protocol satisfying the AC-framework.