Privacy-Preserving Framework for Blockchain-Based Stock Exchange Platform

Abstract

This paper presents a privacy persevering framework for a decentralized stock exchange platform, ensuring anonymity and unlinkability of the investors’ accounts and their respective trading activities. The proposed framework meets these privacy requirements by (i) anonymizing both the unique account identifier (NIN) and balance information through customized data generalization and distortion techniques and (ii) making trading transactions unlinkable to their original investors by ensuring that both the NIN and balance are k -anonymous; i.e., k accounts belonging to different investors have the same balance. Moreover, to ensure long-term unlinkability, the process of anonymization is repeated at regular time intervals (every trading session). In addition to anonymity and unlinkability characteristics, the proposed framework is augmented with traceability and non-repudiation features. The simulation experiments with several market sizes and types confirm the effectiveness of the proposed framework in achieving full k -anonymity. Furthermore, to assess the overhead of the proposed privacy algorithms on the trading execution time, we conduct several experiments considering different anonymity levels k . We compare the transaction execution time of our proposed platform against a traditional non-privacy-preserving blockchain-based stock exchange. The obtained results for the worst-case scenarios show an acceptable execution time overhead.