期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 21, 期 3, 页码 539-554出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2018.2885509
关键词
Edge computing; differential privacy; online learning; big data; trustworthiness; social mutlimedia
资金
- National Science Foundation of China [61672395, 61401169]
The explosive growth of multimedia contents (MCs) in today's mobile social networks has pushed edge computing to face severe security and online big data-processing problems. On the one hand, the edge nodes (ENs) should help mobile users find, cache, and share MCs in the presence of an ever-increasing scale of multimedia big data. On the other hand, how to provide secure MC retrieval schemes to exclude dishonest-and-malicious untrusted ENs and to prevent privacy breaches from honest-but-curious ENs and users is a challenging issue. To tackle these problems, we study the privacy-preserving and trustworthy MCs retrieval system to make personalized MC recommendations from ENs to users with big data support. In our framework, each EN is modeled as a distributed context-aware online learner. ENs collaborate to learn users' preferences based on their contexts and previous behaviors and social intimacy. To support big data analytics, we establish an MC-cluster tree from top to the bottom to handle the dynamically varying cached MC datasets. A differentially private algorithm is proposed to preserve the data privacy among honest-but-curious ENs and users. To guarantee trustworthy edge computing, a trust evaluation mechanism is designed to evaluate the reliability of ENs. We further consider the structure of edge networks to improve the performance of our algorithm. Experimental results validate that our new framework can support increasing multimedia big datasets while striking a balance among privacy-preserving level, Trustworthy level, and caching MC prediction accuracy.
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