An Analytical Study On Privacy Of Personal Data With Artificial Intelligence For Personalized Medicine

Abstract

With the evolution in the field of Information and Communications Technology, information is increasingly being widely shared and available. Data generated due to digitization is derived from a range of sources that involve online banking transactions, search queries, online log information, sensor information, click streams, medical software, and online e-commerce applications. Even though data analytics is beneficial to many applications, the downside is that increasingly digital data and their analysis raise questions regarding the privacy breach of the individual. Human activity at large scale is taking place through electronic media and all these applications gather data in one form or another, including an individual's personal details. Unauthorized use of these personal data or the application of gathered personal data for unfair reasons can threaten a person's privacy. For example, privacy can be infringed when access is provided to a person's sensitive information like salary, caste or a disease condition. In order to preserve privacy, analytic methods can opt to hide sensitive data and focus on non-personal data. But such methods that only aim at privacy protection might not provide correct analytical results and suffer from data quality. Therefore, any privacy-conscious technology in data analytics must enable data quality and privacy protection to coexist.