A Review of Privacy Preserving Data Mining Techniques
Ankita Sharma, Deepak Kumar · Journal of Emerging Technologies and Innovative Research · 2021
Nowadays, privacy-preserving data mining (PPDM) is being studied comprehensively, because of the wide-ranging availability of crucial data available on the internet. There exists a variety of algorithmic techniques for privacy-preserving data mining. The main focus of these algorithms is the mining of required knowledge from large ocean of dataset, at the same time protecting the sensitive information. Privacy Preserving is vital feature of data mining and therefore study of accomplishing some data mining goals without compromising the privacy of the persons is not only challenging but also an assignment of realistic significance. This paper reviews different methods for privacy preserving data mining such as Randomization, K-anonymization, Association rules, Cryptographic technique which are required for maintaining Information sharing and privacy. Studies show that tradeoff between privacy and information loss creates a bottleneck while developing generic solutions. In this paper we present a review of the existing well-organized methodologies in the framework of privacy preservation in data mining.