Multi-Level Reversible Data Anonymization via Compressive Sensing and Data Hiding

Mehmet Yamac, Mete Ahishali, Nikolaos Passalis, Jenni Raitoharju, Bulent Sankur, Moncef Gabbouj

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
5 Downloads (Pure)


Recent advances in intelligent surveillance systems have enabled a new era of smart monitoring in a wide range of applications from health monitoring to homeland security. However, this boom in data gathering, analyzing and sharing brings in also significant privacy concerns. We propose a Compressive Sensing (CS) based data encryption that is capable of both obfuscating selected sensitive parts of documents and compressively sampling, hence encrypting both sensitive and non-sensitive parts of the document. The scheme uses a data hiding technique on CS-encrypted signal to preserve the one-time use obfuscation matrix. The proposed privacy-preserving approach offers a low-cost multi-tier encryption system that provides different levels of reconstruction quality for different classes of users, e.g., semi-authorized, full-authorized. As a case study, we develop a secure video surveillance system and analyze its performance.

Original languageEnglish
Article number9205580
Pages (from-to)1014-1028
Number of pages15
JournalIEEE Transactions on Information Forensics and Security
Early online date2020
Publication statusPublished - 2021
Publication typeA1 Journal article-refereed


  • compressive sensing
  • multi-level encryption
  • Reversible privacy preservation
  • video monitoring

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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