Lightweight edge‐based kNN privacy‐preserving classification scheme in cloud computing circumstance

Abstract

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.

Privacy-preserving parallel kNN classification algorithm using index-based filtering in cloud computing

“…Y. Tan et al proposed a lightweight edge-based privacy-preserving kNN classification algorithm over a hybrid encrypted cloud database [ 24 ]. A data owner can upload his/her database to the cloud server, and an authorized user can send a query to the cloud server to execute kNN queries.…”

“…We explain their comparison with respect to three major factors. First, B. K. Samanthula et al’s work [ 16 ], H. Kim et al’s work [ 17 ], W. Wu et al’s work [ 23 ] and Y. Tan et al’s work [ 24 ] support hiding access pattern, while B. Yao et al’s work [ 21 ] and J. Du and F. Bian’s work [ 25 ] do not support it.…”

STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation

“…But the k-anonymity results in temporal attack due to dynamic data collection [ 17 ]. Light weight edge-based classifier method is applied over the encrypted data using edge computing which has paved the way for data security and user query privacy [ 18 ]. This Anonymity is often considered only for the attributes containing the information for differentiating a particular sample from the simple set.…”