Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2019

Abstract

The development of machine learning technology and visual sensors is promoting the wider applications of face recognition into our daily life. However, if the face features in the servers are abused by the adversary, our privacy and wealth can be faced with great threat. Many security experts have pointed out that, by 3-D-printing technology, the adversary can utilize the leaked face feature data to masquerade others and break the E-bank accounts. Therefore, in this paper, we propose a lightweight privacy-preserving adaptive boosting (AdaBoost) classification framework for face recognition (POR) based on the additive secret sharing and edge computing. First, we improve the current additive secret sharing-based exponentiation and logarithm functions by expanding the effective input range. Then, by utilizing the protocols, two edge servers are deployed to cooperatively complete the ensemble classification of AdaBoost for face recognition. The application of edge computing ensures the efficiency and robustness of POR. Furthermore, we prove the correctness and security of our protocols by theoretic analysis. And experiment results show that, POR can reduce about 58% computation error compared with the existing differential privacy-based framework.

Keywords

Adaptive boosting (AdaBoost), Additive secret sharing, Face recognition, Privacy-preserving

Discipline

Information Security | Numerical Analysis and Scientific Computing

Research Areas

Cybersecurity

Publication

IEEE Internet of Things

Volume

6

Issue

3

First Page

5778

Last Page

5790

ISSN

2327-4662

Identifier

10.1109/JIOT.2019.2905555

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/JIOT.2019.2905555

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