SSF-CDW:为加密云数据仓库实现可扩展、安全和快速的 OLAP 查询

Somchart Fugkeaw, Phatwasin Suksai, Lyhour Hak
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引用次数: 0

摘要

实施基于云的数据仓库来存储敏感或重要的战略数据,会面临一些挑战,主要涉及存储信息的安全性以及云服务器和用户之间的 OLAP 查询交换。虽然加密是保护外包数据的可行解决方案,但将其应用于涉及多维数据、度量和对加密数据进行多维表达式(MDX)操作的 OLAP 查询却存在困难。现有的可搜索加密解决方案不足以处理如此复杂的查询,这使得依赖于高效、安全数据处理和分析的商业智能工具的使用变得更加复杂。本文提出了一种名为 SSF-CDW 的新型隐私保护云数据仓库方案,它有助于为加密云数据仓库提供安全、可扩展的解决方案。与传统的 OLAP 工具相比,我们的 SSF-CDW 改进了只有授权用户才能访问的 OLAP 查询,授权用户可以解密查询结果并提高查询性能。该方法涉及利用对称加密和基于属性的密文策略加密(CP-ABE)来保护多维 OLAP(MOLAP)中建模的维度和事实数据的隐私。为了支持高效的 OLAP 查询执行,我们提出了一种新的数据立方体检索机制,该机制使用的是内存数据库 Redis 模式。该技术通过将查询分解为多个层次来动态编译查询,并将结果合并映射到相应的加密数据立方体。此外,还实现了与加密立方体相关的维度和事实数据的缓存,以提高频繁查询数据的速度。对我们提出的索引搜索策略和其他索引方案进行的实验比较表明,我们的方法在临时和重复 OLAP 查询的搜索速度方面超过了其他技术,同时还保护了查询结果的隐私。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SSF-CDW: achieving scalable, secure, and fast OLAP query for encrypted cloud data warehouse
Implementing a cloud-based data warehouse to store sensitive or critical strategic data presents challenges primarily related to the security of the stored information and the exchange of OLAP queries between the cloud server and users. Although encryption is a viable solution for safeguarding outsourced data, applying it to OLAP queries involving multidimensional data, measures, and Multidimensional Expressions (MDX) operations on encrypted data poses difficulties. Existing searchable encryption solutions are inadequate for handling such complex queries, which complicates the use of business intelligence tools that rely on efficient and secure data processing and analysis.This paper proposes a new privacy-preserving cloud data warehouse scheme called SSF-CDW which facilitates a secure and scalable solution for an encrypted cloud data warehouse. Our SSF-CDW improves the OLAP queries accessible only to authorized users who can decrypt the query results with improved query performance compared to traditional OLAP tools. The approach involves utilizing symmetric encryption and Ciphertext Policy Attribute-Based Encryption (CP-ABE) to protect the privacy of the dimension and fact data modeled in Multidimensional OLAP (MOLAP). To support efficient OLAP query execution, we proposed a new data cube retrieval mechanism using a Redis schema which is an in-memory database. This technique dynamically compiles queries by disassembling them down into multiple levels and consolidates the results mapped to the corresponding encrypted data cube. The caching of dimensional and fact data associated with the encrypted cube is also implemented to improve the speed of frequently queried data. Experimental comparisons between our proposed indexed search strategy and other indexing schemes demonstrate that our approach surpasses alternative techniques in terms of search speed for both ad-hoc and repeated OLAP queries, all while preserving the privacy of the query results.
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