Differentially-private and trustworthy online social multimedia big data retrieval in edge computing

Pan Zhou, Kehao Wang, Jie Xu, Dapeng Wu

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish (US)
Article number8571248
Pages (from-to)539-554
Number of pages16
JournalIEEE Transactions on Multimedia
Issue number3
StatePublished - Mar 2019


  • Big data
  • Differential privacy
  • Edge computing
  • Online learning
  • Social mutlimedia
  • Trustworthiness

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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