A privacy-preserving protocol for continuous and dynamic data collection in IoT enabled mobile app recommendation system (MARS). (15th January 2021)

• Record Type: Journal Article
• Authors: Beg, Saira; Anjum, Adeel; Ahmad, Mansoor; Hussain, Shahid; Ahmad, Ghufran; Khan, Suleman; Choo, Kim-Kwang Raymond
• Is Part Of: Journal of network and computer applications. Volume 174(2021)
• Publication Date: 2021-01-15
• Subjects: Mobile app recommendation system -- Privacy-preserving protocol -- Data collection -- Social-influence -- Reversible integer transform (RIT) -- Internet of Things (IoT); Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2020.102874
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: User trust is an important factor in the success of recommendation systems, including Internet of Things (IoT)-based recommendation systems. However, such trust can be eroded in many different ways (e.g., unauthorized data modifications). Several privacy-preservation schemes have been designed for specific data and/or require strict assumptions (e.g., a private/secure communication channel between client-server and third-party authentication). However, these may limit their application in practice. Hence, in this paper we propose the Reversible Data Transform (RDT) algorithm based privacy-preserving data collection protocol. Our protocol allows us to achieve privacy preservation against beyond the scope processing and does not require a private channel or rely on a third-party authentication. Due to group formation, the disclosure probability of the internal disclosure attack will not be greater than 1/ k . Similarly, the reversible privacy-preserving data mining approach protects beyond the scope processing. Findings from the experimentation demonstrates the utility of the proposed protocol and its potential to be deployed in a mobile app recommendation system.