PhD Defense by Emre Yigitoglu

Event Details
  • Date/Time:
    • Monday March 26, 2018
      12:00 pm - 2:00 pm
  • Location: KACB 3100
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Summary Sentence: Privacy-Aware Big Data Systems and Services in the Internet of Things Era

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Title: Privacy-Aware Big Data Systems and Services in the Internet of Things Era


Emre Yigitoglu

School of Computer Science

College of Computing

Georgia Institute of Technology


Date: Monday, March 26th, 2018

Time: 12:00 - 2:00 pm 

Location: KACB 3100




Dr. Ling Liu (Advisor, School of Computer Science, Georgia Institute of Technology)

Dr. Margaret Loper (Co-Advisor, Georgia Tech Research Institute, Georgia Institute of Technology)

Dr. Shamkant Navathe (School of Computer Science, Georgia Institute of Technology)

Dr. Calton Pu (School of Computer Science, Georgia Institute of Technology)

Dr. Greg Eisenhauer (School of Computer Science, Georgia Institute of Technology)

Dr. Jinpeng Wei (Department of Software and Information Systems, The University of North Caroline at Charlotte)




The Internet of Things (IoT) has been considered to be the next industrial revolution. Connecting physical objects to the internet and equipping things with the powerful data analytic capabilities, the IoT will transform our everyday life from work to travel, leisure to entertainment. It is expected that by 2025 the number of connected devices will reach 100 billion with more than $11 trillion global economic impact. At the same time, the large-scale IoT deployment will be comprised of a huge number of interconnected and heterogeneous wireless and mobile devices, enabling continuous monitoring of activities and events and happenings in our environments and our society. We argue that the large-scale IoT deployment demands privacy-aware big data systems and services that can deliver real-time and life-enhancing experiences while respecting both data privacy and user privacy. This dissertation research contributes original ideas and innovative techniques in the context of the Internet of Things from three perspectives: information service orchestration, application service deployment, and privacy protection.


The first contribution of this dissertation research is the development of ISYMPHONY, a distributed IoT service orchestration architecture for scaling real-time and on-demand IoT services provisioning in large-scale IoT systems while guaranteeing the quality of service with respect to performance, availability, and security. The main idea is to provide an intelligent partition of a real-time IoT computing task into an optimal coordination between the server side processing at IoT gateway and smart edge client-side processing on edge devices.  This computation partitioning and data partitioning architecture enable edge devices and edge clients running on top of edge devices to contribute to the IoT computing tasks based on their computing and communication capacities. 


The second contribution of this dissertation research is the design and development of FOGGY, a framework for continuous automation of IoT application deployment in the three-layer (edge-gateway-Cloud) IoT environment. The core idea of FOGGY is to facilitate dynamic resource provisioning and to automate application deployment in next-generation IoT infrastructure, enabling data processing to be performed at or close to where the data is generated. 


The third technical contribution of this dissertation research is the design and implementation of privacy-preserving models and algorithms for enabling privacy-aware IoT systems and services. Concretely, we developed two systems: StarCloak and PrivacyZone. StarCloak is a query utility-aware, privacy-preserving, and attack-resilient model and a suite of algorithms for mobile objects in the spatially constrained environments, such as moving on the road networks. A comprehensive performance study shows that StarCloak outperforms existing anonymization techniques on both privacy protection and service performance. PrivacyZone is the first spatial trigger-based systems to establish personalized privacy quarantine areas in the physical or cyberspace to enable objects and people to maintain spatial and temporal privacy at a selected area of interests at the selected time interval. PrivacyZone overcomes the applicability limitations of the current privacy-preserving techniques in real-world practices such as mobile permission

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Graduate Studies

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Phd Defense
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Mar 12, 2018 - 11:15am
  • Last Updated: Mar 12, 2018 - 11:15am