Secure Artificial Intelligence of Things for Implicit Group Recommendations

Keping Yu, Zhiwei Guo, Yu Shen*, Wei Wang, Jerry Chun Wei Lin, Takuro Sato

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

94 Citations (Scopus)

Abstract

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications, such as group recommender systems. As the distances between people have been greatly shortened, there has been more general demand for the provision of personalized services aimed at groups instead of individuals. The existing methods for capturing group-level preference features from individuals have mostly been established via aggregation and face two challenges: 1) secure data management workflows are absent and 2) implicit preference feedback is ignored. To tackle these current difficulties, this article proposes secure AIoT for implicit group recommendations (SAIoT-GRs). For the hardware module, a secure Internet of Things structure is developed as the bottom support platform. For the software module, a collaborative Bayesian network model and noncooperative game are introduced as algorithms. This secure AIoT architecture is able to maximize the advantages of the two modules. In addition, a large number of experiments are carried out to evaluate the performance of SAIoT-GR in terms of efficiency and robustness.

Original languageEnglish
Pages (from-to)2698-2707
Number of pages10
JournalIEEE Internet of Things Journal
Volume9
Issue number4
DOIs
Publication statusPublished - 2022 Feb 15

Keywords

  • Bayesian network
  • group recommender systems (GRSs)
  • noncooperative game
  • secure data analytics

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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