SEQURESQL – A FRAMEWORK FOR QUERY OPTIMIZATION AND PRIVACY ON OUTSOURCED DATA (FUDMA JOURNAL OF SCIENCES, 2023)
Database outsourcing has become a trend in the information technology industry because it offers scalability to the enormous amount of digital content stored and generated on a daily basis by individuals and corporations. In large outsourced databases the efficiency of data retrieval, especially as it relates to privacy, remains an open challenge, because traditional query languages cannot work with encrypted data. While several architectures, techniques and tools have beenproffered to ensure that privacy and performance are balanced and optimized, each of these approaches has its limitations. This research proposes a novel framework which focuses on optimizing server-side data retrieval and query efficiency through the use of hash map and AES 128-bit encryption algorithm. The design and implementation of secureSQL is built on the clientside without any alteration to the DBMS structure. SecureSQL model guarantees efficiency and isable to execute 20 out of the 22 Transaction Processing Performance Council (TPC-H) benchmark queries while ensuring privacy. This is proof that it is not restricted to simple query constructs but is able to handle even complex queries involving nested sub queries and joins. The execution time of queries between the client and database on the cloud is minimized, with a 0(1) time complexity as is evident in the comparative performance analysis between secureSQL and the traditional method. This is quantified using numerical results.
Keywords: Cloud Database, Data Privacy, Hash Map, Query Processing, Encryption, DaaS, Relational Database.