An Investigation of Data Privacy and Utility Using Machine Learning as a Gauge

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An Investigation of Data Privacy and Utility Using Machine Learning as a Gauge

  1. 1. “AN INVESTIGATION OF DATA PRIVACY AND UTILITY USING MACHINE LEARNING AS A GAUGE” Kato Mivule, D.Sc. Candidate, Computer Science Bowie State University Thursday, April 10, 2014 @ 3:30 – 4:45 PM, CSB - Room 210 Kato Mivule is a doctoral candidate in the Department of Computer Science at The Bowie State University, advised by Dr. Claude Turner. His research interests consist of data privacy and utility preservation, machine learning, data mining, database systems, and multi-agent learning systems. His current work includes investigating an optimal equilibrium between data privacy and utility needs using machine learning classifiers as a gauge. He got his Associates in Computer Information Systems from Longview Community College, in Missouri, USA, and Bachelors in Computer Science from the University of Missouri – Kansas City, USA. Abstract: Organizations that transact in large amounts of data have to comply with federal laws to guarantee that the privacy of individuals and other sensitive data is not compromised. However, during the data privacy process, data loses its utility – a measure of how useful a privatized dataset is to the user of that dataset. Researchers have noted that attaining an optimal balance between data privacy and utility needs is an NP-hard challenge, thus intractable. Therefore we present the classification error gauge (x-CEG) model, a quantification approach that employs machine learning classification to gauge data utility based on the classification error. Additionally, we present SIED, an innovative conceptual framework that takes a holistic approach to the data privacy and utility engineering process by outlining the specifications, implementation, evaluation, and dissemination phases in the data privacy process. Contact Dr. Soo-Yeon Ji (sji@bowiestate.edu) if you have any question. SPRING2014SEMINARSERIES

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