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A multi‐dimensional index for privacy‐preserving queries in cloud computing
Author(s) -
Xu Hui,
Ding Xiaofeng,
Jin Hai,
Yu Qing
Publication year - 2019
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.5458
Subject(s) - computer science , cloud computing , differential privacy , encryption , server , information sensitivity , information privacy , cryptography , index (typography) , information leakage , data mining , computer security , database , information retrieval , computer network , world wide web , operating system
Summary 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.