Privacy-preserving data mining in the malicious model

Most of the cryptographic work in privacy-preserving distributed data mining deals with semi-honest adversaries, which are assumed to follow the prescribed protocol but try to infer private information using the messages they receive during the protocol. Although the semi-honest model is reasonable in some cases, it is unrealistic to assume that adversaries will always follow the protocols exactly. In particular, malicious adversaries could deviate arbitrarily from their prescribed protocols. Secure protocols that are developed against malicious adversaries require utilisation of complex techniques. Clearly, protocols that can withstand malicious adversaries provide more security. However, there is an obvious 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 performance and security in privacy-preserving distributed data mining algorithms in the two models. In order to make a realistic comparison, we enhance commonly used subprotocols 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. Copyright © 2008, Inderscience Publishers.