Enabling Better Medical Image Classification Through Secure Collaboration

Vaidya, Jaideep; Tulpule, Bhakti

doi:10.1109/icip.2007.4380054

Cited by 3 publications

(1 citation statement)

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“…Furthermore, the combination of signal processing with cryptographic techniques in order to protect privacy is an active area of research [13]; among others, solutions for recognizing speech on encrypted signals [33] or image classification and object recognition on encrypted images [37,2] have been proposed. The latter work describes a solution to a problem that is complementary to the one discussed in the present paper (and can be used in conjunction with our solution): locating rectangular regions on an encrypted image that show human faces.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Face Recognition

Erkin

Franz

Guajardo

et al. 2009

Lecture Notes in Computer Science

445

405

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Abstract. Face recognition is increasingly deployed as a means to unobtrusively verify the identity of people. The widespread use of biometrics raises important privacy concerns, in particular if the biometric matching process is performed at a central or untrusted server, and calls for the implementation of Privacy-Enhancing Technologies. In this paper we propose for the first time a strongly privacy-enhanced face recognition system, which allows to efficiently hide both the biometrics and the result from the server that performs the matching operation, by using techniques from secure multiparty computation. We consider a scenario where one party provides a face image, while another party has access to a database of facial templates. Our protocol allows to jointly run the standard Eigenfaces recognition algorithm in such a way that the first party cannot learn from the execution of the protocol more than basic parameters of the database, while the second party does not learn the input image or the result of the recognition process. At the core of our protocol lies an efficient protocol for securely comparing two Paillerencrypted numbers. We show through extensive experiments that the system can be run efficiently on conventional hardware.

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