Privacy-preserving Computation over Encrypted Vectors

Rui Hu, Wenxiu Ding*, Zheng Yan

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

2 Sitaatiot (Scopus)
102 Lataukset (Pure)


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.
Otsikko2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
ISBN (elektroninen)978-1-7281-8298-8
DOI - pysyväislinkit
TilaJulkaistu - jouluk. 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Global Communications Conference - Taipei, Taiwan
Kesto: 7 jouluk. 202011 jouluk. 2020


NimiIEEE Global Communications Conference
ISSN (painettu)2334-0983
ISSN (elektroninen)2576-6813


ConferenceIEEE Global Communications Conference


Sukella tutkimusaiheisiin 'Privacy-preserving Computation over Encrypted Vectors'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä