Clustering-based efficient privacy-preserving face recognition scheme without compromising accuracy

Meng Liu, Hongsheng Hu, Haolong Xiang, Chi Yang, Lingjuan Lyu, Xuyun Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Recently, biometric identification has been extensively used for border control. Some face recognition systems have been designed based on Internet of Things. But the rich personal information contained in face images can cause severe privacy breach and abuse issues during the process of identification if a biometric system has compromised by insiders or external security attacks. Encrypting the query face image is the state-of-the-art solution to protect an individual’s privacy but incurs huge computational cost and poses a big challenge on time-critical identification applications. However, due to their high computational complexity, existing methods fail to handle large-scale biometric repositories where a target face is searched. In this article, we propose an efficient privacy-preserving face recognition scheme based on clustering. Concretely, our approach innovatively matches an encrypted face query against clustered faces in the repository to save computational cost while guaranteeing identification accuracy via a novel multi-matching scheme. To the best of our knowledge, our scheme is the first to reduce the computational complexity from O(M) in existing methods to approximate O(√M), where M is the size of a face repository. Extensive experiments on real-world datasets have shown the effectiveness and efficiency of our scheme.
Original languageEnglish
Article number31
Pages (from-to)1-27
Number of pages27
JournalACM Transactions on Sensor Networks
Issue number3
Early online date21 Jun 2021
Publication statusPublished - Aug 2021


  • face recognition
  • Secure two-party computation
  • computational complexity
  • clustering
  • privacy-preserving
  • Face recognition
  • Privacy-preserving
  • Clustering
  • Computational complexity


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