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[期刊论文]

SecureAD: A Secure Video Anomaly Detection Framework on Convolutional Neural Network in Edge Computing Environment

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author:

Cheng, Hang (Cheng, Hang.) [1] (Scholars:程航) | Liu, Ximeng (Liu, Ximeng.) [2] (Scholars:刘西蒙) | Wang, Huaxiong (Wang, Huaxiong.) [3] | Unfold

Indexed by:

EI Scopus SCIE

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 article, 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.

Keyword:

anomaly detection Bloom filter CNN Privacy-preserving secret sharing

Community:

  • [ 1 ] [Cheng, Hang]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China
  • [ 2 ] [Wang, Meiqing]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China
  • [ 3 ] [Cheng, Hang]Nanyang Technol Univ, Sch Phys & Math Sci, Singapore 639798, Singapore
  • [ 4 ] [Wang, Huaxiong]Nanyang Technol Univ, Sch Phys & Math Sci, Singapore 639798, Singapore
  • [ 5 ] [Liu, Ximeng]Fuzhou Univ, Coll Math & Comp Sci, Key Lab Informat Secur Network Syst, Fuzhou 350108, Peoples R China
  • [ 6 ] [Fang, Yan]Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Peoples R China
  • [ 7 ] [Zhao, Xiaopeng]East China Normal Univ, Sch Comp Sci & Software Engn, Dept Cryptog & Cyber Secur, Shanghai 200062, Peoples R China

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Source :

IEEE TRANSACTIONS ON CLOUD COMPUTING

ISSN: 2168-7161

Year: 2022

Issue: 2

Volume: 10

Page: 1413-1427

6 . 5

JCR@2022

5 . 3 0 0

JCR@2023

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:61

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 9

30 Days PV: 1

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