Enhancing the Security using Blockchain-Based Privacy-Preserving Framework for Stock Exchange Platform - Rohith Davuluri, Preetham Roy Patibandla, Adithya Kalastri, Supriya Kommu, Suguna Sri Andukuri, Sheema.Sk (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2023)
This article introduces a privacy-preserving architecture for a distributed stock exchange platform, which keeps investors' accounts and trades private and untraceable. In order to fulfil these privacy needs, the proposed framework (i) uses specialised data generalisation and distortion techniques to conceal the unique account identifier (NIN) and balance information, and (ii) prevents trading transactions from being traced back to their original investors by making the NIN and balance k-anonymous, meaning that k accounts belonging to different investors share the same balance. In addition, the anonymization procedure is carried out on a periodic basis (after each trading session) to provide permanent anonymity. The suggested framework includes not only anonymity and unlinkability but also traceability and non-repudiation. The simulation studies on a variety of sized and kinds of markets verify the efficiency of the proposed framework in obtaining complete k-anonymity. Furthermore, we undertake a number of tests with varying degrees of anonymity k to evaluate the impact of the proposed privacy algorithms on the trade execution time. We evaluate our proposed platform by looking at how quickly trades are executed in comparison to a standard stock exchange built on a blockchain that does not protect user privacy. Even under the worst-case circumstances, the findings obtained reveal a reasonable increase in execution time