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Purpose: In today's world, data is gathered without any particular objective in mind; every action taken by a computer, or a human being is documented, and the data is studied in the future, if it is considered important to do so. The data will be subjected to several steps for analysis by a variety of parties, which raises the issue of trust in this context. There is a risk that the organizations involved in the analytical stages may abuse the data, which might contain private or sensitive information. Consequently, data privacy considerations should be carefully considered at this time. Methodology/Findings: A definition of "data privacy" is the practice of limiting access to information according to how important it is. People are usually very comfortable giving out their names to strangers, but they'll wait to give out their mobile phone numbers until they're more familiar with the individual. In this era of digital technology, important personal information is often the target of individuals' efforts to protect their data. From a business's point of view, data privacy encompasses more than just employees' and consumers' private information. Data privacy issues are often believed to be a barrier to the widespread adoption of AI and ML-driven technology. The reason for this is that ML can only be trained and tested on very large data sets. Implications to Theory, Practice and Policy: Imagine a world where trust is impossible to establish; here is where Blockchain technology might be useful. Blockchain uses the data anonymously. In this study, we provide a solution that ensures data security by integrating Blockchain technology with machine learning (Alfandi et al., 2020).
Shanmugam et al. (Mon,) studied this question.