Big Data Differential Privacy Preservation

Tuesday, February 19, 2019 - 10:00
Blakeslee Room
Jingyi Wang

Nowadays, many domains of intelligent systems such as smart metering, intelligent transportation, health care, sensor/data aggregation, crowd sensing etc., typically collect huge amounts of data for decision making, where the data may include individual or sensitive information. Since a vast amount of information is analyzed, released and calculated by the system to make smart decisions, big data plays a key role as an advanced analysis technique providing more efficient and complete solutions. However, data privacy breaches during any stage of these large scale systems, either during collection or big data analysis can be an undesirable loss of privacy for the participants and for the entire system. In this work, a series of privacy preserving data analytic and processing methodologies through data driven optimization are developed. The integration of the data analysis and data privacy preservation techniques provides the most desirable solutions for different big data scenarios with various application-specific requirements


Jingyi Wang received her B.S. degree in Physics from Nankai University, China, in 2012 and M.S. degree in electrical and computer engineering from Auburn University, Auburn, AL, in 2015. She has been working towards her Ph.D. degree in the Department of Electrical and Computer Engineering at University of Houston, Houston, TX, since August 2015. Her research interests include the privacy preservation of cybersecurity and big data analytics. Her work on Primary Users’ Operational Privacy Preservation via Data-Driven Optimization won Best Paper Award in Globecom 2017