A game theory based repeated rational secret sharing scheme for privacy preserving distributed data mining

Collaborative data mining has become very useful today with the immense increase in the amount of data collected and the increase in competition. This in turn increases the need to preserve the participants' privacy. There have been a number of approaches proposed that use Secret Sharing for privacy preservation for Secure Multiparty Computation (SMC) in different setups and applications. The different multiparty scenarios may have parties that are semi-honest, rational or malicious. A number of approaches have been proposed for semi honest parties in this setup. The problem however is that in reality we have to deal with parties that act in their self-interest and are rational. These rational parties may try and attain maximum gain without disrupting the protocol. Also these parties if cautioned would correct themselves to have maximum individual gain in the future. Thus we propose a new practical game theoretic approach with three novel punishment policies with the primary advantage that it avoids the use of expensive techniques like homomorphic encryption. Our proposed approach is applicable to the secret sharing scheme among rational parties in distributed data mining. We have analysed theoretically the proposed novel punishment policies for this approach. We have also empirically evaluated and implemented our scheme using Java. We compare the punishment policies proposed in terms of the number of rounds required to attain the Nash equilibrium with eventually no bad rational nodes with different percentage of initial bad nodes.