A schema for protecting the integrity of databases

Unauthorized changes to databases can result in significant losses for organizations as well as individuals. Watermarking can be used to protect the integrity of databases against unauthorized alterations. Prior work focused on watermarking database tables or relations. Malicious alteration cannot be detected in all cases. In this paper we argue that watermarking database indexes in addition to the database tables would improve the detection of unauthorized alterations. Usually, each database table in commercial applications has more than one index attached to it. Thus, watermarking the database table and all its indexes improve the likelihood of detecting malicious attacks. In general, watermarking different indexes like R-trees, B-trees, Hashes, require different watermarking techniques and exploit different redundancies in the underlying data structure. This diversity in watermarking techniques contributes to the overall integrity of the databases.Traditional relational watermarks introduce some error to the watermarked values and thus cannot be applied to all attributes. This paper proposes a novel watermarking scheme for R-tree data structures that does not change the values of the attributes. Moreover, the watermark does not change the size of the R-tree. The proposed technique takes advantage of the fact that R-trees do not put conditions on the order of entries inside the node. In the proposed scheme, entries inside R-tree nodes are rearranged, relative to a “secret” initial order (a secret key), in a way that corresponds to the value of the watermark.To achieve that, we propose a one-to-one mapping between all possible permutations of entries in the R-tree node and all possible values of the watermark. Without loss of generality, watermarks are assumed to be numeric values. The proposed mapping employs a numbering system that uses variable base with factorial value.The detection rate of the malicious attacks depends on the nature of the attack, distribution of the data, and the size of the R-tree node. Our extensive analysis and experimental results showed that the proposed technique detects data alteration with high probability (that reaches up to 99%) on real datasets using reasonable node sizes and attack model. The watermark insertion and extraction are mainly main memory operations, and thus, have minimal effect on the cost of R-tree operations.