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Lightweight edge‐based kNN privacy‐preserving classification scheme in cloud computing circumstance
Author(s) -
Tan Yejin,
Wu Wei,
Liu Jian,
Wang Huimei,
Xian Ming
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5804
Subject(s) - computer science , cloud computing , encryption , scheme (mathematics) , enhanced data rates for gsm evolution , outsourcing , information privacy , focus (optics) , edge computing , data security , database , computer security , operating system , artificial intelligence , mathematical analysis , physics , mathematics , optics , political science , law
Summary Because mobile terminals have limited computing and storage resources, individuals tend to outsource their data generated from mobile devices to clouds to do data operations. However, utilization of the abundant computation and storage resources of clouds may pose a threat to user's private data. In this paper, we focus on the issue of encrypted k‐nearest neighbor (kNN) classification on the cloud. In the past few years, many solutions were proposed to protect the user's privacy and data security. Unfortunately, most privacy‐preserving data mining schemes are not lightweight, which are not practical in real‐world applications. To solve this issue, we proposed a lightweight edge‐based kNN (EBkNN) classification scheme over encrypted cloud database utilizing edge computing technology. Our proposed scheme can provide several security guarantees: (i) user's data security, (ii) user's query privacy, and (iii) data access patterns. We analyzed the security of our scheme utilizing the semi‐honest security model and evaluated the performance using a synthetic dataset. The experiment results indicate that our scheme is more lightweight than the state‐of‐the‐art scheme.