Ph.D. Research Proficiency: Masoud Nosrati

Masoud Nosrati
Monday, November 23, 2020 - 8:00am
Event Type: 

Location: Zoom

Verifying the Correctness of Analytic Query Results

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. A significant problem of 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; still 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. The practicality of our approaches in run time is confirmed through extensive experiments.

Committee: Ying Cai (major professor), Wallapak Tavanapong, Qi Li, Shawn Dorius, and Cassandra Dorius