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Open AccessArticle

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

School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
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Sensors 2018, 18(12), 4383; https://doi.org/10.3390/s18124383
Received: 30 October 2018 / Revised: 4 December 2018 / Accepted: 8 December 2018 / Published: 11 December 2018
(This article belongs to the Special Issue Social Sensing)
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’ 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’ 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 k-means clustering algorithm to select the core users among trust relationships, and the core users’ 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’ 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. View Full-Text
Keywords: privacy intrusion; social sensing; trust relationship; k-means clustering; core user; information disclosure privacy intrusion; social sensing; trust relationship; k-means clustering; core user; information disclosure
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Wu, H.; Li, M.; Zhang, H. UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion. Sensors 2018, 18, 4383.

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