Two methods for privacy preserving data mining with malicious participants

Privacy preserving data mining addresses the need of multiple parties with private inputs to run a data mining algorithm and learn the results over the combined data without revealing any unnecessary information. Most of the existing cryptographic solutions to privacy-preserving data mining assume semi-honest participants. In theory, these solutions can be extended to the malicious model using standard techniques like commitment schemes and zero-knowledge proofs. However, these techniques are often expensive, especially when the data sizes are large. In this paper, we investigate alternative ways to convert solutions in the semi-honest model to the malicious model. We take two classical solutions as examples, one of which can be extended to the malicious model with only slight modifications while another requires a careful redesign of the protocol. In both cases, our solutions for the malicious model are much more efficient than the zero-knowledge proofs based solutions.