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

Saira Beg, Adeel Anjum, Mansoor Ahmad, Shahid Hussain, Ghufran Ahmed, Suleman Khan, Kim-Kwang Raymond Choo

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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/. 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.
Original languageEnglish
Article number102874
Number of pages10
JournalJournal of Network and Computer Applications
Volume174
Early online date4 Nov 2020
DOIs
Publication statusPublished - 15 Jan 2021

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