查询DAS模型中的加密字符数据

Nian Liu, Yajian Zhou, Xinxin Niu, Yixian Yang
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引用次数: 6

摘要

人们越来越关注数据库即服务(DAS)体系结构。在DAS模型中,对数据库中加密数据进行查询的传统方案是在数据库中构造高效索引。这样可以保证查询效率。然而,在字符数据的查询中经常使用模糊搜索。由于数据加密,这将非常困难。即使明文的微小变化,密文也会相当明显。因此,构建高效的加密字符串索引是至关重要的。针对这些问题,本文提出了一种关系数据库中字符数据的加密方案。在这个方案中,将分别考虑char或varchar数据类型和clob数据类型。对于char或varchar类型的数据,建立了基于索引的双重过滤。索引包括两部分:索引的第一部分用于确定原字符串中存在的字符的集合,第二部分存储字符位置的特征值。index的第一部分实现了第一次过滤,它将过滤所有包含不准确字符的记录。index的第二部分用于验证字符串的位置。这是第二次过滤。对于clob类型的数据,将存储所有字符串的位置,而不是原始字符数据。该方案避免了不必要的解密和数据传输。从而提高查询效率,支持模糊搜索。
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Querying encrypted character data in DAS model
There has been increasing concern about Databaseas-a-Service(DAS) architectures. In the DAS model, the conventional scheme for query over encrypted data in database is to construct efficient index in database. This can ensure query efficiency. However, the fuzzy search is often used in the query of character data. It will be very tough owing to data encryption. Even the tiny changes of plaintext, the ciphtext will be quite distinct. Therefore, it is crucial to construct efficient index of encrypted character strings. To address these problems, a cryptographic scheme of character data in relational database is proposed in this paper. In this scheme, the char or varchar data type and clob data type will be considered respectively. For the data of char or varchar type, an index based double filtration is established. The index includes two parts: the first part of index is used to determine the set of existed characters in original string, and the second part stores the characteristic value of the characters' positions. The first part of index implements the first filtration, which will filter all the records include inaccurate characters. The second part of index is used to verify the positions of character strings. It's the second filtration. For the data of clob type, instead of the original character data, the positions of all the character strings will be stored. This scheme avoids needless decryption and data transfer. Consequently, better query efficiency is achieved and fuzzy search is well supported.
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