Partially Materialized Digest Scheme: An Efficient Verification Method for Outsourced Databases

• Main Authors: MOURATIDIS, Kyriakos, SACHARIDIS, Dimitris, PANG, Hwee Hwa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Query result verification; Authentication in outsourced databases; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/745; https://ink.library.smu.edu.sg/context/sis_research/article/1744/viewcontent/PMD_VLDBJ.pdf
• Institution: Singapore Management University

In the outsourced database model, a data owner publishes her database through a third-party server; i.e., the server hosts the data and answers user queries on behalf of the owner. Since the server may not be trusted, or may be compromised, users need a means to verify that answers received are both authentic and complete, i.e., that the returned data have not been tampered with, and that no qualifying results have been omitted. We propose a result verification approach for one-dimensional queries, called Partially Materialized Digest scheme (PMD), that applies to both static and dynamic databases. PMD uses separate indexes for the data and for their associated verification information, and only partially materializes the latter. In contrast with previous work, PMD avoids unnecessary costs when processing queries that do not request verification, achieving the performance of an ordinary index (e.g., a B+-tree). On the other hand, when an authenticity and completeness proof is required, PMD outperforms the existing state-of-the-art technique by a wide margin, as we demonstrate analytically and experimentally. Furthermore, we design two verification methods for spatial queries. The first, termed Merkle R-tree (MR-tree), extends the conventional approach of embedding authentication information into the data index (i.e., an R-tree). The second, called Partially Materialized KD-tree (PMKD), follows the PMD paradigm using separate data and verification indexes. An empirical evaluation with real data shows that the PMD methodology is superior to the traditional approach for spatial queries too.