Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2019

Abstract

Person re-identification (Re-ID) has attracted extensive attention due to its potential to identify a person of interest from different surveillance videos. With the increasing amount of the surveillance videos, high computation and storage costs have posed a great challenge for the resource-constrained users. In recent years, the cloud storage services have made a large volume of video data outsourcing become possible. However, person Re-ID over outsourced surveillance videos could lead to a security threat, i.e., the privacy leakage of the innocent person in these videos. Therefore, we propose an efFicient privAcy-preseRving peRson Re-ID Scheme (FARRIS) over outsourced surveillance videos, which can ensure the privacy of the detected person while providing the person Re-ID service. Specifically, FARRIS exploits the convolutional neural network (CNN) and kernels based supervised hashing (KSH) to extract the efficient person Re-ID feature. Then, we design a secret sharing based Hamming distance computation protocol to allow cloud servers to calculate similarities among obfuscated feature indexes. Furthermore, a dual Merkle hash trees based verification is proposed, which permits users to validate the correctness of the matching results. The extensive experimental results and security analysis demonstrate that FARRIS can work efficiently, without compromising the privacy of the involved person. CCBY

Keywords

Merkle hash tree, Person re-identification, Privacy-Preserving, Secret sharing, Secure Hamming distance

Discipline

Computer and Systems Architecture | Software Engineering

Publication

IEEE Transactions on Dependable and Secure Computing

First Page

1

Last Page

19

ISSN

1545-5971

Identifier

10.1109/TDSC.2019.2923653

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

https://doi.org/10.1109/TDSC.2019.2923653

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