Abstract

Due to the wide deployment of sensitive information on the internet, privacy preserving data mining has been studied extensively in recent years. The emerging privacy concern has become a major obstacle in storing and sharing of medical data. The proliferation of medical data can be highly useful, but it must be performed in a way that preserves patients privacy. This is not straightforward, because the proliferated data need to be protected against several privacy threats. Various algorithms have been designed for privacy-preserving data mining that can be classified into three categories i.e., privacy by policy, privacy by statistics, and privacy by cryptography, however, the privacy concerns and data utilization requirements on different parts of medical data may be quite different. In this paper, we present a survey of the state-of-the-art algorithms that have been proposed for publishing medical data in a privacy preserving way. We review algorithms like; Randomization, k-anonymization, and distributed privacy-preserving data mining etc., derive insights on their operation, and highlight their advantages and disadvantages. We also provide discussion of the computational and hypothetical boundaries associated with privacy-preservation over high dimensional data sets. Musavir Hassan | Muheet Ahmed Butt | Majid Zaman"Privacy Preserving Data Mining for Healthcare Record: A Survey of Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7191.pdf http://www.ijtsrd.com/computer-science/data-miining/7191/privacy-preserving-data-mining-for-healthcare-record-a-survey-of-algorithms/musavir-hassan

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