Privacy-preserving Computation over Encrypted Vectors
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Author
Date
2020-12
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Language
en
Pages
6
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2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings, IEEE Global Communications Conference
Abstract
Cloud computing allows users to outsource massive amounts of data to a cloud server for storage and analysis, which breaks the bottleneck of limited local resources. However, it makes user data exposed and possibly be accessed by unauthorized entities. Owing to privacy concern, users are inclined to upload encrypted data to a cloud server, but encryption limits operations over original data and affects access to a processing result. Though lots of schemes have been proposed to achieve some basic operations over encrypted data, it still lacks the research on the dot product of encrypted vectors. In this paper, we propose two privacy-preserving dot product schemes based on a dual server model, which can flexibly support single-user access and multiuser access to a final data processing result. Furthermore, we extend them to achieve privacy-preserving Support Vector Machine (SVM) prediction algorithm. Finally, we give security analysis of our proposed schemes and demonstrate their availability and practicality through simulation and comparison with existing works.Description
Keywords
cloud computing, privacy preserving, encrypted vector, dot product, access control, SVM
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Citation
Hu , R , Ding , W & Yan , Z 2020 , Privacy-preserving Computation over Encrypted Vectors . in 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings . , 9322329 , IEEE Global Communications Conference , IEEE , IEEE Global Communications Conference , Taipei , Taiwan, Republic of China , 07/12/2020 . https://doi.org/10.1109/GLOBECOM42002.2020.9322329
