Privacy-Preserving Data Mining Applications in the Malicious Model

Although the semi-honest model is reasonable in some cases, it is unrealistic to assume that adversaries will al- ways follow the protocols exactly. In particular, malicious adversaries could deviate arbitrarily from their prescribed protocols. Clearly, protocols that can withstand malicious adversaries provide more security. However, there is an ob- vious trade-off: protocols that are secure against malicious adversaries are generally more expensive than those secure against semi-honest adversaries only. In this paper, our goal is to make an analysis of trade-offs between perfor- mance and security in privacy-preserving distributed data mining algorithms in the two models. In order to make a realistic comparison, we enhance commonly used subpro- tocols that are secure in the semi-honest model with zero knowledge proofs to be secure in the malicious model. We compare the performance of these protocols in both models.