Enabling privacy-preserving SQL-log analysis - Fang, Hongjie
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The topic of this scientific work is enabling privacy-preserving SQL-log analysis. Our goal is to find out an anonymization method, which aims to protect the privacy of SQL-log, meanwhile it has as less effect on the query plans as possible. Our experimental database and queries come from the famous TPC-H benchmark, in which we have the well constructed schema of original tables and all the 22 queries are designed to test as many aspects of the database as possible. We use existing DBMSs (Oracle and MySQL) to carry out our approach and test the performance of the anonymization method in the…mehr

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Produktbeschreibung
The topic of this scientific work is enabling privacy-preserving SQL-log analysis. Our goal is to find out an anonymization method, which aims to protect the privacy of SQL-log, meanwhile it has as less effect on the query plans as possible. Our experimental database and queries come from the famous TPC-H benchmark, in which we have the well constructed schema of original tables and all the 22 queries are designed to test as many aspects of the database as possible. We use existing DBMSs (Oracle and MySQL) to carry out our approach and test the performance of the anonymization method in the two DBMSs. As final results, our original anonymization method has successfully anonymized 95.45% of the queries in MySQL, which have always the same query plans. Experiments within Oracle need to be adjusted 3 times to improve the ratio of well anonymized query statements from 50% to 89.47%.
Autorenporträt
The author studied Computer Science and Technology at the Beijing University of Posts and Telecommunications, has a Master in Informatics from Karlsruhe Institute of Technology and is now a PHD Student at the University of Siegen, Chair for Embedded Systems, with the current research topic: Execution Environment for Integrated Architecture in TCMS.