School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China.
Sensors (Basel). 2018 Dec 11;18(12):4383. doi: 10.3390/s18124383.
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 -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.
隐私侵犯已成为当前信任感知社交感知的主要瓶颈，因为由于物联网（IoT）的普及，在线社交媒体允许任何人大量披露个人信息。由于收集用户的个人数据和披露行为，最先进的社交感知仍然受到严重的隐私威胁，这可能会因个性化的数据整合而引起用户隐私问题。在本文中，我们提出了一种信任感知模型，称为具有信任的用户和项目相似性模型，用于不同种类的（UISTD），以增强社交感知的个性化，同时减少用户的隐私问题。UISTD 利用用户对用户的相似性和项目对项目的相似性，生成具有共同标签的多种个性化项目。UISTD 还应用了一种改进的均值聚类算法来选择信任关系中的核心用户，并且核心用户的偏好和披露行为将被视为预测的披露模式。在三个真实数据集上的实验结果表明，目标用户更有可能：（1）关注信任关系中的核心用户的不同类型的项目和披露行为，从而优于比较方法；（2）以较低的入侵意识和隐私担忧来披露更多信息。
