A NOVEL APPROACH OF KNN CLASSIFIER OVER SEMANTICALLY SECURE ENCRYPTED DATA

Computer Engineering

In the emerging cloud computing paradigm, data owners become increasingly motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. For the consideration of users’ privacy, sensitive data have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models.I focus on solving the classification problem over encrypted data. In particular, I propose a novel approach of KNN classifier over semantically secure encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user’s input query, and hides the data access patterns.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.