Novel Approach to Provide Context Aware Privacy for Consumer Using IOT and Cloud

Abstract

Context-awareness is emerging to be more significant in the present world as it supports to newlineadapt the functionalities and consumer interfaces of applications based on environmental newlineconditions or consumer preferences. Today we can see huge applications of IoT and cloud newlinecomputing in various sectors such as in agriculture, Industry, Medical and Healthcare, newlineSmart Home Appliances, etc. As IoT and cloud computing are used in everyday consumer newlineservices, the human factors is part of context-awareness. These days one of the major issues newlinein context awareness is privacy which is a very challenging task. newlineThe research work undertaken defines secrecy maintenance issues similar to optimizing newlinetasks, thereby verifying their accuracy and optimization capabilities through a hypothetical newlinestudy. Many optimal issues arise while preserving one s privacy and these optimal issues newlineare to be addressed as linear programming issues. By addressing linear programming issues, newlinean effective context-aware privacy-preserving algorithm was developed that uses an active newlinedefense strategy to determine how to release a user s current context to enhance the quality newlineof service regarding context-aware applications while maintaining secrecy. CAPP newlineoutperforms other standard methodologies in lengthy simulations of actual data. newlineAdditionally, the minimax learning algorithm optimizes the policy users and improves the newlinesatisfaction threshold of the context-aware applications. Moreover, a cloud-based approach newlineis introduced in the work to protect the user s privacy from third parties. The obtained newlineperformance measures are compared with existing approaches in terms of privacy policy newlinebreaches, context sensitivity, satisfaction threshold, adversary power, and convergence newlinespeed for online and offline attacks newline