Privacy Preservation within Stock Market

Abstract

In stock market, the data contain sensitive information, and undesired disclosure of this information can lead to various attacks, thus breaching privacy and causing severe damage to the users. We propose a data privacy preservation scheme within stock market, where rough set theory concepts are utilized to anonymize the data during data transfer in stock market. The proposed data privacy preservation scheme is tested by considering several case studies of a stock market. The simulation results show the effectiveness of proposed scheme.