A novel cryptographic protocol for privacy preserving classification over distributed encrypted databases
Tóm tắt
Recent progression in Information Technology facilitated the collection and storage of large amounts of data to be accessed by multiple parties in a distributed manner. Privacy is an important concern while mining sensitive data. In a distributed data scenario, when the data is available in encrypted form, mining it without sharing original data among the involved parties is a challenging task. One of the activities in privacy preserving data mining is privacy preserving data classification. In this work, we propose a privacy preserving
$$k$$
-NN data classification technique for distributed encrypted databases. Our classification approach uses a private Jaccard similarity measure, which is based on privacy equality testing protocol. We also discuss the security analysis of the proposed protocol with respect to various cryptographic attacks.
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