UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

• Main Authors: Hongchen Wu, Mingyang Li, Huaxiang Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Sensors
• Subjects: privacy intrusion; social sensing; trust relationship; <i>k</i>-means clustering; core user; information disclosure
• Online Access: https://www.mdpi.com/1424-8220/18/12/4383

Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users&#8217; personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users&#8217; privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified <i>k</i>-means clustering algorithm to select the core users among trust relationships, and the core users&#8217; preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users&#8217; interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern.