Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2022

Abstract

Anomaly detection offers a powerful approach to identifying unusual activities and uncommon behaviors in real-world video scenes. At present, convolutional neural networks (CNN) have been widely used to tackle anomalous events detection, which mainly rely on its stronger ability of feature representation than traditional hand-crafted features. However, massive video data and high cost of CNN model training are a challenge to achieve satisfactory detection results for resource-limited users. In this paper, we propose a secure video anomaly detection framework (SecureAD) based on CNN. Specifically, we introduce additive secret sharing to design several calculation protocols for achieving safe CNN training and video anomaly detection. Besides, we propose a Bloom filter based fine-grained access control policy to authenticate legitimate users, without leaking the privacy of raw personal attributes. In addition, edge computing instead of cloud computing is integrated into the architecture to reduce response time between servers and users in an outsourced environment. Finally, we prove that the proposed SecureAD achieves secure video anomaly detection without compromising the privacy of the related data. Also, the simulation results demonstrate the effectiveness and security of our SecureAD.

Keywords

Privacy-preserving, anomaly detection, Bloom filter, CNN, secret sharing

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Cloud Computing

Volume

107

Issue

2

First Page

1413

Last Page

1427

ISSN

2168-7161

Identifier

10.1109/TCC.2020.2990946

Publisher

IEEE Computer Society

Embargo Period

5-13-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TCC.2020.2990946

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