A SURVEY ON PRIVACY PRESERVING TECHNIQUE USING K- NEAREST NEIGHBOR CLASSIFICATION
Rashmi Sheshrao Kodane, K. Bagde
Mar 5, 2016
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Journal
International Journal of Engineering Sciences and Research Technology
Full text
Semantic Scholar
Key Takeaway Key Takeaway: Our privacy-preserving k-NN classification protocol effectively protects user privacy and data confidentiality while enabling data mining on encrypted data in the cloud.
Abstract
Data mining is a powerful new technique to discover knowledge within the large amount of the data. Also data mining is the process of discovering meaningful new relationship, patterns and trends by passing large amounts of data stored in corpus, using pattern recognition technologies as well as statistical and mathematical techniques. To protect user privacy, various privacy-preserving classification techniques have been proposed over the past decade. The existing techniques are not applicable to outsourced database environments where the data resides in encrypted form on a third-party server. This paper proposed a novel privacy-preserving k-NN classification protocol over encrypted data in the cloud. Our protocol protects the confidentiality of the data, user’s input query, and hides the data access patterns. We also evaluated the performance of our protocol under different parameter settings.