A multi‐dimensional index for privacy‐preserving queries in cloud computing

Abstract

Although cloud computing saves the cost of enterprises and individuals to manage data in various applications on public cloud servers, it also causes the problem of the leakage of critical personal information and sensitive data. Therefore, the issues about data privacy of the published personal information have become a challenging problem. Most of the existing methods focus on cryptography-based techniques by encrypting the sensitive data before publishing. However, these techniques are either too computation expensive or only some authorized users can get access to the encrypted cloud data. The consequence is that the search response time is not satisfactory and the sharing extent of the cloud data is becoming limited. Motivated by this, we propose a perturbation-based method to preserve the privacy of data in cloud. To accelerate the query processing without privacy breaches, a high-efficiency multi-dimensional index is built for answering range counting queries under differential privacy, which embeds privacy-preserving R-tree index in Content Addressable Network. Experimental results demonstrate that our methods not only protect data privacy in different degree, but also accelerate the query speed efficiently. Compared with the existing method Quad-opt, our method provides about 20% better data utility under the same differential privacy principles.

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

“…As an essential data‐mining operation, the kNN query can be used as a vital component of many data mining approaches 7 . In the past few years, to support the kNN query over encrypted cloud databases, many types of research have been proposed 7‐19 . Those works usually involve three parties: the cloud server (CS), the database owner (DO), and the query user (QU).…”