Verifying the Correctness of Analytic Query Results

TL;DR

This paper addresses verifying the correctness of complex analytic query results in outsourced data scenarios, proposing new verification methods for ranking-based queries like top-k, range, and KNN.

Contribution

It introduces two novel verification approaches, one-signature and multi-signature, tailored for complex analytic queries involving ranking functions.

Findings

The proposed methods effectively verify top-k, range, and KNN queries.

Experimental results demonstrate high accuracy and efficiency.

The approaches outperform existing verification techniques for analytic queries.

Abstract

Data outsourcing is a cost-effective solution for data owners to tackle issues such as large volumes of data, huge number of users, and intensive computation needed for data analysis. They can simply upload their databases to a cloud and let it perform all management works, including query processing. One problem with this service model is how query issuers can verify the query results they receive are indeed correct. This concern is legitimate because, as a third party, clouds may not be fully trustworthy, and as a large data center, clouds are ideal targets for hackers. There has been significant work on query result verification, but most consider only simple queries where query results can be attained by checking the raw data against the query conditions directly. In this paper, we consider the problem of enabling users to verify the correctness of the results of analytic queries. Unlike simple queries, analytic queries involve ranking functions to score a database, which makes it difficult to build data structures for verification purposes. We propose two approaches, namely one-signature and multi-signature, and show that they work well on three representative types of analytic queries, including top-k, range, and KNN queries, through both analysis and experiments.