A Technique for Maintaining Attribute-based Privacy Implementing Blockchain and Machine Learning

Abstract

Any wireless network is built on the mutually reinforcing pillars of privacy and security. Security measures often cover data connections, physical security, outside threats, and internal node operations. Whereas privacy covers the selective exchange of data among a network’s various entities. To provide privacy to networks, a large number of algorithms have been proposed by researchers in the past. Most of these algorithms utilize Graph-based anonymization approaches like l-diversity, k-anonymity, etc. These models do not scale well. Thus, this text proposes a machine learning-based block chain-powered privacy preservation protocol. The proposed protocol will perform attribute-based privacy with high efficiency due to the use of block chain architecture and will improve the overall Quality of Service of the network due to the integration of machine learning in the system.