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Date:  Thu, April 27, 2017
Time:  11:30am
Location:  Holmes 287
Speaker:  Dr. Yao Zheng, EE Dr. Guohui Zhang

Privacy Preservation for Cloud-Based Data Sharing and Data Analytics

Dr. Yao Zheng

Data Privacy is a globally recognized human right for individuals to control the access tot heir personal information, and bar the negative consequences from the use of this information. As communication technologies progress, the means to protect data privacy must also evolve to address new challenges come into view. Our research aims to develop privacy protection frameworks and techniques for the emerging cloud-based data services, in particular privacy-preserving algorithms and protocols for the cloud-based data sharing and data analytics services. Our research has three main contributions. First, to capture users' privacy expectations in the cloud computing paradigm, we conceptually divide personal data into two categories, i.e., visible data and invisible data. The visible data refer to the information users intentionally create, upload to, and share through the cloud; the invisible data refer to users' information retained in the cloud that is aggregated, analyzed, and repurposed without their knowledge or understanding. Second, to address users' privacy concerns raised by cloud computing, we propose two privacy protection frameworks, namely individual control and use limitation. The individual control framework emphasizes users' capability to govern the access to the visible data stored in the cloud. The use limitation framework emphasizes users' expectation to remain anonymous when the invisible data are aggregated and analyzed by cloud-based data services. Finally, we investigate various techniques to accommodate the new privacy protection frame-works, in the context of four cloud-based data services: personal health record sharing, location-based proximity test, link recommendation for social networks, and face tagging for photo management applications.

Developing Smart Management Strategies for High Occupancy Toll Lane System Operations

Dr. Guohui Zhang

Dramatically increasing travel demands and insufficient traffic facility supplies have induced severe traffic congestion problems. It is of practical importance to manage the existing transportation facilities more efficiently with advanced traffic control and management technologies in addition to travel demand control High Occupancy Toll (HOT) lane is one of the most effective traffic management systems for freeway congestion mitigation. In this presentation, HOT lane system operation mechanisms are introduced, and a self-adaptive tolling strategy is presented to optimize HOT lane operations. The toll rates are computed based on toll changing patterns and real-time traffic measurements on HOT and General Purpose (GP) lanes. By dynamically adjusting toll rates, traffic allocations between GP and HOT lanes can be controlled and overall system operation efficiency can be maximized. VISSIM-based simulation experimental tests show the proposed tolling strategy performs reasonably well in optimizing HOT lane system operations under various traffic conditions.