Abstract 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.

中文翻译：

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基于云的物联网服务的安全驱动混合协同推荐方法

摘要 物联网（IoT）系统和云技术的快速发展为推荐系统方便用户的日常生活铺平了道路。然而，随之而来的环境攻击、软件攻击等网络安全风险也不容忽视。因此，推荐系统中的安全问题成为基于云的物联网服务的严峻挑战。此外，现有的协同推荐算法大多主要关注用户-项目交互关系，很少考虑用户-用户或项目-项目共现关系，这可能会影响预测精度。为了克服上述缺点，本文提出了一种安全驱动的混合协同推荐方法，以更具可扩展性和安全性的方式处理云可访问的大规模物联网服务。我们的提议将基于分解的潜在因子模型与基于邻居的协作模型相结合，不仅可以挖掘用户-服务交互关系，还可以挖掘用户-用户和服务-服务共现关系。此外，采用本地敏感散列（LSH）技术来加速邻居搜索并基于散列映射为安全考虑保留用户的敏感信息。最后，实验结果表明，所提出的方法可以在保证信息安全的同时提高预测精度。采用本地敏感散列（LSH）技术来加速邻居搜索，并基于散列映射为安全考虑保留用户的敏感信息。最后，实验结果表明，所提出的方法可以在保证信息安全的同时提高预测精度。采用本地敏感散列（LSH）技术来加速邻居搜索，并基于散列映射为安全考虑保留用户的敏感信息。最后，实验结果表明，所提出的方法可以在保证信息安全的同时提高预测精度。