Lightweight and Privacy-Preserving IoT Service Recommendation Based on Learning to Hash

In the Internet of Things (IoT) environment, user-service interaction data are often stored in multiple distributed platforms. In this situation, recommender systems need to integrate the distributed user-service interaction data across different platforms for making a comprehensive recommendation decision, during which user privacy is probably disclosed. Moreover, as user-service interaction records accumulate over time, they significantly reduce the efficiency of recommendations. To tackle these issues, we propose a lightweight and privacy-preserving service recommendation approach named SerRec<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L2H</inf>. In SerRec<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L2H</inf>, we employ Learning to Hash (L2H) to encapsulate sensitive user-service interaction data into less-sensitive user indices, which facilitates identifying users with similar preferences efficiently for accurate recommendations. We then validate the feasibility of our proposed SerRec<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L2H</inf> approach through massive experiments conducted on the popular WS-DREAM dataset. The comparative analysis with other competitive approaches demonstrates that our proposal surpasses other approaches in terms of recommendation accuracy and efficiency while protecting user privacy.