research-article

Image Feature Extraction with Homomorphic Encryption on Integer Vector

Authors:

Honggang WuAuthors Info & Claims

ICMLSC '17: Proceedings of the 2017 International Conference on Machine Learning and Soft Computing

Pages 111 - 116

https://doi.org/10.1145/3036290.3036301

Published: 13 January 2017 Publication History

Abstract

With the amount of user-contributed image data increasing, it is a potential threat for users that everyone may have the access to gain privacy information. To reduce the possibility of the loss of real information, this paper combines homomorphic encryption scheme and image feature extraction to provide a guarantee for users' privacy. In this paper, the whole system model mainly consists of three parts, including social network service providers (SP), the Interested party (IP) and the applications. Except for the image preprocessing phase, the main operations of feature extraction are conducted in ciphertext domain, which means only SP has the access to the privacy of the users. The extraction algorithm is used to obtain a multi-dimensional histogram descriptor as image feature for each image. As a result, the histogram descriptor can be extracted correctly in encrypted domain in an acceptable time. Besides, the extracted feature can represent the image effectively because of relatively high accuracy. Additionally, many different applications can be conducted by using the encrypted features because of the support of our encryption scheme.

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Cited By

Dhar ARoy ABiswas SIslam BGummeson JLee SGao JXing G(2022)Studying the Security Threats of Partially Processed Deep Neural Inference Data in an IoT DeviceProceedings of the 20th ACM Conference on Embedded Networked Sensor Systems10.1145/3560905.3568091(845-846)Online publication date: 6-Nov-2022
https://dl.acm.org/doi/10.1145/3560905.3568091

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cover image ACM Other conferences

ICMLSC '17: Proceedings of the 2017 International Conference on Machine Learning and Soft Computing

January 2017

233 pages

ISBN:9781450348287

DOI:10.1145/3036290

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 January 2017

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Research-article
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Funding Sources

the National Natural Science Foundation of China
the International Science and Technology Cooperation and Exchange Program of Sichuan Province, China
China Postdoctoral Science Foundation funded project

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ICMLSC '17

ICMLSC '17: 2017 International Conference on Machine Learning and Soft Computing

January 13 - 16, 2017

Ho Chi Minh City, Vietnam

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Cited By

Dhar ARoy ABiswas SIslam BGummeson JLee SGao JXing G(2022)Studying the Security Threats of Partially Processed Deep Neural Inference Data in an IoT DeviceProceedings of the 20th ACM Conference on Embedded Networked Sensor Systems10.1145/3560905.3568091(845-846)Online publication date: 6-Nov-2022
https://dl.acm.org/doi/10.1145/3560905.3568091

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