Privacy-preserving identification for monitoring images
Camera sensors embedded in monitor units or mobile phones make it easy to capture various personal images in daily life. Machine learning especially deep learning provides an elegant way to identify images (e.g., person re-identification, face recognition, facial expression recognition). However, a...
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sg-smu-ink.sis_research-96502024-02-08T06:30:04Z Privacy-preserving identification for monitoring images ZHAO, Bowen LI, Xiaoguo Camera sensors embedded in monitor units or mobile phones make it easy to capture various personal images in daily life. Machine learning especially deep learning provides an elegant way to identify images (e.g., person re-identification, face recognition, facial expression recognition). However, a personal image usually involves an amount of sensitive data, such as identity, face, and facial expression. Accordingly, image identification poses severe challenges of privacy leakage for persons' identities, face data, facial expressions, etc. Either GDPR (General Data Protection Regulation) or EDPS (European Data Protection Supervisor) stipulates that monitoring images involve private data and are easy to intrude on the fundamental right to privacy. In this chapter, we first sort out the privacy concerns in monitoring image identification and then formalize privacy-preserving identification for monitoring images. Next, we give a general framework to achieve privacy-preserving monitoring image identification and discuss privacy-preserving person re-identification based on the proposed framework. Finally, we conclude the research challenges and attempt to foresee some new research directions in privacy-preserving monitoring image identification. 2024-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/8647 info:doi/10.1049/PBPC061E_ch10 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Graphics and Human Computer Interfaces Information Security |
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Graphics and Human Computer Interfaces Information Security ZHAO, Bowen LI, Xiaoguo Privacy-preserving identification for monitoring images |
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Camera sensors embedded in monitor units or mobile phones make it easy to capture various personal images in daily life. Machine learning especially deep learning provides an elegant way to identify images (e.g., person re-identification, face recognition, facial expression recognition). However, a personal image usually involves an amount of sensitive data, such as identity, face, and facial expression. Accordingly, image identification poses severe challenges of privacy leakage for persons' identities, face data, facial expressions, etc. Either GDPR (General Data Protection Regulation) or EDPS (European Data Protection Supervisor) stipulates that monitoring images involve private data and are easy to intrude on the fundamental right to privacy. In this chapter, we first sort out the privacy concerns in monitoring image identification and then formalize privacy-preserving identification for monitoring images. Next, we give a general framework to achieve privacy-preserving monitoring image identification and discuss privacy-preserving person re-identification based on the proposed framework. Finally, we conclude the research challenges and attempt to foresee some new research directions in privacy-preserving monitoring image identification. |
format |
text |
author |
ZHAO, Bowen LI, Xiaoguo |
author_facet |
ZHAO, Bowen LI, Xiaoguo |
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ZHAO, Bowen |
title |
Privacy-preserving identification for monitoring images |
title_short |
Privacy-preserving identification for monitoring images |
title_full |
Privacy-preserving identification for monitoring images |
title_fullStr |
Privacy-preserving identification for monitoring images |
title_full_unstemmed |
Privacy-preserving identification for monitoring images |
title_sort |
privacy-preserving identification for monitoring images |
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Institutional Knowledge at Singapore Management University |
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2024 |
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https://ink.library.smu.edu.sg/sis_research/8647 |
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