Security-Driven hybrid collaborative recommendation method for cloud-based iot services

Shunmei Meng, Zijian Gao, Qianmu Li, Hao Wang, Hong Ning Dai, Lianyong Qi*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

19 Citations (Scopus)


The rapid development of IoT (Internet of Things) systems and cloud techniques has paved the way for recommender systems to facilitate the daily life of users. However, the accompanying cybersecurity risks, such as environmental attacks and software attacks, must not be ignored. Thus, the security problem in recommender systems becomes a serious challenge for cloud-based IoT services. Moreover, most of existing collaborative recommendation algorithms mainly focus on user-item interaction relationships but seldom consider user-user or item-item co-occurrence relationships, which may affect prediction accuracy. To overcome the above shortcomings, this paper proposes a security-driven hybrid collaborative recommendation method to deal with the large-scale IoT services accessible by clouds in a more scalable and secure manner. Our proposal integrates the factorization-based latent factor model with the neighbor-based collaborative model to mine not only user-service interaction relationships but also user-user and service-service co-occurrence relationships. Moreover, the local sensitive hash (LSH) technique is adopted to speed up the neighbor searching and preserve users’ sensitive information for security concerns based on hash mapping. Finally, experiment results demonstrate that the proposed method can improve prediction accuracy while guaranteeing information security.

Original languageEnglish
Article number101950
JournalComputers and Security
Publication statusPublished - Oct 2020

Scopus Subject Areas

  • Computer Science(all)
  • Law

User-Defined Keywords

  • Collaborative recommendation
  • IoT services
  • LSH
  • MF
  • Security


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