Secure Artificial Intelligence of Things for Implicit Group Recommendations

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

Research output: Contribution to journalArticlepeer-review

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: secure data management workflows are absent, and implicit preference feedback is ignored. To tackle these current difficulties, this paper proposes secure AIoT for implicit group recommendations (SAIoT-GR). For the hardware module, a secure IoT 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
JournalIEEE Internet of Things Journal
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Artificial intelligence
  • Bayesian network
  • Collaboration
  • Computer architecture
  • Games
  • Hardware
  • Internet of Things
  • Secure data analytics
  • Software
  • group recommender systems
  • noncooperative game.

ASJC Scopus subject areas

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

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