Role of Data Science in Enhancing Privacy in E-Healthcare Systems

Manas Kumar Yogi; Muktineni Urjitha  Bala; K. Venkateswari

Abstract

The test is that while learning and data science are basic empowering agents of productivity and viability in present day associations, exploiting them often implies entrusting delicate data to outer sellers. The overall privacy model will in general be binary: either the data science as a specialist resource has the association's full trust and gets immediate admittance to the data, or they do not have the trust and they get no entrance by any means. This "full trust" model makes an either/or circumstance that sets associations up to need to pick security or advancement. Fortunately, there are a few promising data privacy innovations that can facilitate that strain. These are "fractional trust" models that give more space to both security and advancement. In this study we try to throw sufficient light on the various works related to privacy in healthcare systems and also discuss the various dimensions of privacy where suitable data science mechanisms are been deployed for the greater good of patients who are always worried about their sensitive data being leaked.

Abstract

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