A Systematic Review on Privacy Preservation Techniques for Smart Contracts in Blockchain using Machine Learning

Abstract

Smart contracts are algorithmic descriptions of self-executing transaction protocols, that get automatically executed, based on the information provided by the entities involved. They are written in a specialized programming language for a specific domain and must adhere to relevant legislation. In addition to being formally correct and unambiguous, smart contracts rely on the trustworthiness, safety, and security of the platform on which they are executed. One of the emerging challenges is to protect the privacy of data. Privacy preserving in smart contracts refers to the ability of a smart contract to protect the personal information of the parties involved. Ensuring the security of sensitive data within Ethereum smart contracts is crucial due to the frequent use of these contracts for facilitating exchanges or transactions of sensitive information. If proper measures are not taken to protect this data, it may be susceptible to unauthorized access or disclosure, potentially leading to detrimental outcomes for the parties involved. This literature review work embarks on a comprehensive examination of the evolving landscape of data privacy within the realm of smart contracts, transcending the intrinsic transparency that characterizes blockchain technology.