Secure Artificial Intelligence of Things for Implicit Group Recommendations

TL;DR

This paper introduces a secure AIoT framework for implicit group recommendations, combining hardware security with advanced algorithms to improve privacy, efficiency, and robustness in social computing applications.

Contribution

It proposes a novel secure AIoT architecture integrating hardware and software modules, including Bayesian networks and game theory, for enhanced group recommendation systems.

Findings

Demonstrates improved efficiency in group recommendation tasks.

Shows increased robustness against data security threats.

Validates the effectiveness of the proposed architecture through extensive experiments.

Abstract

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications such as group recommender systems. As distance among people has been greatly shortened, it has been a more general demand to provide personalized services to groups instead of individuals. In order to capture group-level preference features from individuals, existing methods were mostly established via aggregation and face two aspects of challenges: secure data management workflow is absent, and implicit preference feedbacks is ignored. To tackle current difficulties, this paper proposes secure Artificial Intelligence of Things for implicit Group Recommendations (SAIoT-GR). As for hardware module, a secure IoT structure is developed as the bottom support platform. As for software module, collaborative Bayesian network model and non-cooperative game are can be introduced as algorithms. Such a 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 the SAIoT-GR in terms of efficiency and robustness.