e-PRNU: Encrypted Domain PRNU-Based Camera Attribution for Preserving Privacy

Mohanty, M; Zhang, M; Asghar, MR; Russello, G

e-PRNU: Encrypted Domain PRNU-Based Camera Attribution for Preserving Privacy

Mohanty, M

Zhang, M Asghar, MR Russello, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Dependable and Secure Computing, 2020, PP, (99), pp. 1-1
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Mohanty, M https://orcid.org/0000-0002-0258-4586
dc.contributor.author	Zhang, M
dc.contributor.author	Asghar, MR
dc.contributor.author	Russello, G
dc.date.accessioned	2020-11-25T05:38:01Z
dc.date.available	2020-11-25T05:38:01Z
dc.date.issued	2020
dc.identifier.citation	IEEE Transactions on Dependable and Secure Computing, 2020, PP, (99), pp. 1-1
dc.identifier.issn	1545-5971
dc.identifier.issn	1941-0018
dc.identifier.uri	http://hdl.handle.net/10453/144340
dc.description.abstract	IEEE Photo Response Non-Uniformity (PRNU) noise-based source camera attribution is a popular digital forensic method. In this method, a camera fingerprint computed from a set of known images of the camera is matched against the extracted noise of an anonymous questionable image to find out if the camera had taken the anonymous image. The possibility of privacy leak, however, is one of the main concerns of the PRNU-based method. Using the camera fingerprint (or the extracted noise), an adversary can identify the owner of the camera by matching the fingerprint with the noise of an image (or with the fingerprint computed from a set of images) crawled from a social media account. In this article, we address this privacy concern by encrypting both the fingerprint and the noise using the Boneh-Goh-Nissim (BGN) encryption scheme, and performing the matching in encrypted domain. To overcome leakage of privacy from the content of an image that is used in the fingerprint calculation, we compute the fingerprint within a trusted environment, such as ARM TrustZone. We present e-PRNU that aims at minimizing privacy loss and allows authorized forensic experts to perform camera attribution. The security analysis shows that the proposed approach is semantically secure.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Dependable and Secure Computing
dc.relation.isbasedon	10.1109/TDSC.2019.2892448
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0803 Computer Software, 0804 Data Format, 0805 Distributed Computing
dc.subject.classification	Strategic, Defence & Security Studies
dc.title	e-PRNU: Encrypted Domain PRNU-Based Camera Attribution for Preserving Privacy
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0803 Computer Software
utslib.for	0804 Data Format
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-11-25T05:37:55Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

IEEE Photo Response Non-Uniformity (PRNU) noise-based source camera attribution is a popular digital forensic method. In this method, a camera fingerprint computed from a set of known images of the camera is matched against the extracted noise of an anonymous questionable image to find out if the camera had taken the anonymous image. The possibility of privacy leak, however, is one of the main concerns of the PRNU-based method. Using the camera fingerprint (or the extracted noise), an adversary can identify the owner of the camera by matching the fingerprint with the noise of an image (or with the fingerprint computed from a set of images) crawled from a social media account. In this article, we address this privacy concern by encrypting both the fingerprint and the noise using the Boneh-Goh-Nissim (BGN) encryption scheme, and performing the matching in encrypted domain. To overcome leakage of privacy from the content of an image that is used in the fingerprint calculation, we compute the fingerprint within a trusted environment, such as ARM TrustZone. We present e-PRNU that aims at minimizing privacy loss and allows authorized forensic experts to perform camera attribution. The security analysis shows that the proposed approach is semantically secure.

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http://hdl.handle.net/10453/144340