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Privacy-Preserving Data Analytics for Big Data Applications
Wednesday, February 10, 2016 - 3:00pm to 4:00pm
Refreshments and discussion following. Public is welcome.
Speaker: Yanming Gong
Abstract: Big data has driven innovation, productivity, efficiency, and growth in many domains, creating enormous benefits for the global economy. However, with massive data and advanced data analytical techniques, far more information can be inferred than most people have anticipated at the time of data collection/publication. Traditional privacy-preserving techniques are either insufficient against such new privacy attacks or preventing reasonable data usage. We need a secure and privacy-preserving solution which allows people to learn information as it was intended and stops people from learning information in ways it was not.
In this talk, I will first describe privacy challenges in current big data applications , and then focus on protecting the privacy of biomedical "big" data in healthcare systems. Our work attempts to design a practical, secure, and privacy-preserving framework for utilizing healthcare data which are distributed among multiple parties. Our framework is general and can be used for multiple learning algorithms. Moreover, I will briefly introduce my work on security and privacy in other big data applications such as mobile computing and smart metering.
Yanmin Gong received the B.Eng. degree in electronics and information engineering from Huazhong University of Science and Technology, China, and the M.S. degree in electrical engineering from Tsinghua University, China, in 2009 and 2012, respectively. She is currently a PhD candidate in electrical and computer engineering at the University of Florida. Her current research interests focus on security and privacy in big data with applications in healthcare, mobile computing, and cyber-physical systems.