Privacy-preserving outsourced image feature extraction

Dongmei Li; Xiaolei Dong; Zhenfu Cao; Haijiang Wang

doi:10.1016/j.jisa.2019.03.020

Privacy-preserving outsourced image feature extraction

Dongmei Li, Xiaolei Dong^*, Zhenfu Cao, Haijiang Wang

^*Corresponding author for this work

Software Engineering Institute

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

Privacy-protected signal and image processing for outsourced computing has hence recently been popular. We present a framework for privacy-preserving outsourced image feature Extraction(POIFE) in the semi-honest setting. Also we prove that the construction is simulation-based secure under the semi-honest adversaries. In our scheme, the user can be flexible to outsource his computation to the cloud without real-time online. Compared with the previous works, our solution improves privacy.

Original language	English
Pages (from-to)	59-64
Number of pages	6
Journal	Journal of Information Security and Applications
Volume	47
DOIs	https://doi.org/10.1016/j.jisa.2019.03.020
State	Published - Aug 2019

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10.1016/j.jisa.2019.03.020

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title = "Privacy-preserving outsourced image feature extraction",

abstract = "Privacy-protected signal and image processing for outsourced computing has hence recently been popular. We present a framework for privacy-preserving outsourced image feature Extraction(POIFE) in the semi-honest setting. Also we prove that the construction is simulation-based secure under the semi-honest adversaries. In our scheme, the user can be flexible to outsource his computation to the cloud without real-time online. Compared with the previous works, our solution improves privacy.",

author = "Dongmei Li and Xiaolei Dong and Zhenfu Cao and Haijiang Wang",

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AU - Li, Dongmei

AU - Dong, Xiaolei

AU - Cao, Zhenfu

AU - Wang, Haijiang

PY - 2019/8

Y1 - 2019/8

N2 - Privacy-protected signal and image processing for outsourced computing has hence recently been popular. We present a framework for privacy-preserving outsourced image feature Extraction(POIFE) in the semi-honest setting. Also we prove that the construction is simulation-based secure under the semi-honest adversaries. In our scheme, the user can be flexible to outsource his computation to the cloud without real-time online. Compared with the previous works, our solution improves privacy.

AB - Privacy-protected signal and image processing for outsourced computing has hence recently been popular. We present a framework for privacy-preserving outsourced image feature Extraction(POIFE) in the semi-honest setting. Also we prove that the construction is simulation-based secure under the semi-honest adversaries. In our scheme, the user can be flexible to outsource his computation to the cloud without real-time online. Compared with the previous works, our solution improves privacy.

UR - https://www.scopus.com/pages/publications/85064455248

U2 - 10.1016/j.jisa.2019.03.020

DO - 10.1016/j.jisa.2019.03.020

M3 - 文章

AN - SCOPUS:85064455248

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VL - 47

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EP - 64

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ER -

Privacy-preserving outsourced image feature extraction

Abstract

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