Privacy Preserving Data Mining for Healthcare Record: A Survey of Algorithms

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 prolifera medical data can be highly useful, but it must be performed in a way that preserves patient's privacy. This is not straightforward, because the proliferated data need to be protected against several privacy threats. Various algorithms have been des 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 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.