Big Data Privacy Preservation for Cyber-Physical Systems
| dc.contributor.advisor | Pan, Miao | |
| dc.contributor.committeeMember | Han, Zhu | |
| dc.contributor.committeeMember | Prasad, Saurabh | |
| dc.contributor.committeeMember | Faghih, Rose T. | |
| dc.contributor.committeeMember | Qian, Lijun | |
| dc.creator | Wang, Jingyi | |
| dc.date.accessioned | 2020-01-04T01:30:55Z | |
| dc.date.created | May 2019 | |
| dc.date.issued | 2019-05 | |
| dc.date.submitted | May 2019 | |
| dc.date.updated | 2020-01-04T01:30:56Z | |
| dc.description.abstract | Cyber-physical systems (CPS) often referred as ``next generation of engineered systems" are sensing and communication systems that offer tight integration of computation and networking capabilities to monitor and control entities in the physical world. The advent of cloud computing technologies, artificial intelligence and machine learning models has extensively contributed to these multidimensional and complex systems by facilitating a systematic transformation of massive data into information. Though CPS have infiltrated into many areas due to their advantages, big data analytics and privacy are major considerations for building efficient and high-confidence CPS. Many domains of CPS 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 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 for CPS. 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. This work focuses on effective big data analytics for CPS and addresses the privacy issues that arise in various CPS applications. Because of their numerous advantages, CPS and its communication networks inevitably become the targets of attackers and malicious users either during data collection, data storage, data transmission, or data processing and computation, keeping users' information at risk. Given these challenges, this work endeavors to develop a series of privacy preserving data analytic and processing methodologies through data driven optimization based on differential privacy; and focuses on effectively integrating the data analysis and data privacy preservation techniques to provide the most desirable solutions for the state-of-the-art CPS with various application-specific requirements. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Jingyi Wang, Sai Mounika Errapotu, Yanmin Gong, Lijun Qian, Riku Jantti, Miao Pan and Zhu Han, “Data-Driven Optimization Based Primary Users Operational Privacy Preservation”, IEEE Transactions on Cognitive Communications and Networking, Vol. 4, No. 2, pp. 357-367, June 2018. And in: Wang, Jingyi, Xinyue Zhang, Qixun Zhang, Ming Li, Yuanxiong Guo, Zhiyong Feng, and Miao Pan. "Data-Driven Spectrum Trading with Secondary Users' Differential Privacy Preservation." IEEE Transactions on Dependable and Secure Computing (2019). And in: Wang, Jingyi, Xinyue Zhang, Haijun Zhang, Hai Lin, Hideki Tode, Miao Pan, and Zhu Han. "Data-Driven Optimization for Utility Providers with Differential Privacy of Users' Energy Profile." In 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1-6. IEEE, 2018. And in: Zhang, Xinyue, Jingyi Wang, Hongning Li, Yuanxiong Guo, Qingqi Pei, Pan Li, and Miao Pan. "Data-Driven Caching with Users' Local Differential Privacy in Information-Centric Networks." In 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1-6. IEEE, 2018. And in: Wang, Jingyi, Xinyue Zhang, Qixun Zhang, Ming Li, Yuanxiong Guo, Zhiyong Feng, and Miao Pan. "Data-Driven Spectrum Trading with Secondary Users' Differential Privacy Preservation." IEEE Transactions on Dependable and Secure Computing (2019). | |
| dc.identifier.uri | https://hdl.handle.net/10657/5735 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Cyber-Physical Systems | |
| dc.subject | Big data analytics | |
| dc.subject | Privacy Preservation | |
| dc.title | Big Data Privacy Preservation for Cyber-Physical Systems | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| local.embargo.lift | 2021-05-01 | |
| local.embargo.terms | 2021-05-01 | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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