A privacy-preserved indexing schema in DaaS model for range queries

• 作者: Hao Renzhi Li Jun Wu Guangjun
• 来源期刊: 高技术通讯（英文版）
• 年，卷（期）: 2020,（4）
• 页码范围: 448-454
• 页数: 7页
• 语种: 英文
• DOI: 10.3772/j.issn.1006-6748.2020.04.013

摘要

In a database-as-a-service ( DaaS) model, a data owner stores data in a database server of a service provider, and the DaaS adopts the encryption for data privacy and indexing for data query. However, an attacker can obtain original data' s statistical information and distribution via the inde-xing distribution from the database of the service provider. In this work, a novel indexing schema is proposed to satisfy privacy-preserved data management requirements, in which an attacker cannot obtain data source distribution or statistic information from the index. The approach includes 2 parts:the Hash-based indexing for encrypted data and correctness verification for range queries. The evalu-ation results demonstrate that the approach can hide statistical information of encrypted data distribu-tion while can also obtain correct answers for range queries. Meanwhile, the approach can achieve nearly 10 times and 35 times improvement on encrypted data publishing and indexing respectively, compared with the start-of-the-art method order-preserving Hash-based function ( OPHF) .