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author:

Zhang, Chuan (Zhang, Chuan.) [1] | Hu, Chenfei (Hu, Chenfei.) [2] | Wu, Tong (Wu, Tong.) [3] | Zhu, Liehuang (Zhu, Liehuang.) [4] | Liu, Ximeng (Liu, Ximeng.) [5] (Scholars:刘西蒙)

Indexed by:

EI Scopus SCIE

Abstract:

The neural network has been widely used to train predictive models for applications such as image processing, disease prediction, and face recognition. To produce more accurate models, powerful third parties (e.g., clouds) are usually employed to collect data from a large number of users, which however may raise concerns about user privacy. In this paper, we propose an Efficient and Privacy-preserving Neural Network scheme, named EPNN, to deal with the privacy issues in cloud-based neural networks. EPNN is designed based on a two-cloud model and techniques of data perturbation and additively homomorphic cryptosystem. This scheme enables two clouds to cooperatively perform neural network training and prediction in a privacy-preserving manner and significantly reduces the computation and communication overhead among participating entities. Through a detailed analysis, we demonstrate the security of EPNN. Extensive experiments based on real-world datasets show EPNN is more efficient than existing schemes in terms of computational costs and communication overhead.

Keyword:

additively homomorphic cryptosystem cloud environments Computational modeling Cryptography Data models data perturbation Data privacy neural network Neural networks Predictive models Privacy-preserving Training

Community:

  • [ 1 ] [Zhang, Chuan]Beijing Inst Technol, Sch Cyberspace Sci & Technol, Beijing 100811, Peoples R China
  • [ 2 ] [Wu, Tong]Beijing Inst Technol, Sch Cyberspace Sci & Technol, Beijing 100811, Peoples R China
  • [ 3 ] [Zhu, Liehuang]Beijing Inst Technol, Sch Cyberspace Sci & Technol, Beijing 100811, Peoples R China
  • [ 4 ] [Hu, Chenfei]Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100811, Peoples R China
  • [ 5 ] [Liu, Ximeng]Fuzhou Univ, Singapore Management Univ, Sch Informat Syst, Coll Math & Comp Sci,Fujian Prov Key Lab Informat, Fuzhou 350025, Fujian, Peoples R China

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Source :

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING

ISSN: 1545-5971

Year: 2023

Issue: 5

Volume: 20

Page: 4245-4257

7 . 0

JCR@2023

7 . 0 0 0

JCR@2023

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count: 84

SCOPUS Cited Count: 48

ESI Highly Cited Papers on the List: 5 Unfold All

  • 2025-1
  • 2024-11
  • 2024-9
  • 2024-7
  • 2024-5

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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