Compressive Sensing based Multi-class Privacy-preserving Cloud Computing

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


In this paper, we design the multi-class privacy-preserving cloud computing scheme (MPCC) leveraging compressive sensing for compact sensor data representation and secrecy for data encryption. The proposed scheme achieves two-class secrecy, one for superuser who can retrieve the exact sensor data, and the other for semi-authorized user who is only able to obtain the statistical data such as mean, variance, etc. MPCC scheme allows computationally expensive sparse signal recovery to be performed at cloud without compromising the confidentiality of data to the cloud service providers. In this way, it mitigates the issues in data transmission, energy and storage caused by massive IoT sensor data as well as the increasing concerns about IoT data privacy in cloud computing. Compared with the state-of-the-art schemes, we show that MPCC scheme not only has lower computational complexity at the IoT sensor device and data consumer, but also is proved to be secure against ciphertext-only attack.
Original languageEnglish
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
Number of pages6
Publication date2020
Article number9348093
ISBN (Electronic)9781728182988
Publication statusPublished - 2020
Event2020 IEEE Global Communications Conference - Taipei, Taiwan + Online (Hybrid), Taipei, Taiwan
Duration: 7 Dec 202011 Dec 2020


Conference2020 IEEE Global Communications Conference
LocationTaipei, Taiwan + Online (Hybrid)
Internet address


  • IoT
  • cloud computing
  • compressed sensing
  • encryption
  • privacy preserving


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