{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86625"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86625","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Towards Privacy-Preserving and Secure Crowd Sensing in the Internet of Things","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Miao, Chenglin"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Su, Lu","Computer Science and Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:35:45Z","date_published":"2025-02-21T21:35:45Z","updated_at":"2026-07-27T19:05:32Z","subjects":["computer science","computer engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86625","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Su, Lu","Computer Science and Engineering"]},{"key":"dc:creator","label":"Author","values":["Miao, Chenglin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:35:45Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science","computer engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86625"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Recent years have witnessed the rise of Internet of Things (IoT), a newly emergent networking paradigm that connects humans and the physical-world through ubiquitous sensing, computing, and communicating devices. Driven by the ubiquitous and interconnected sensing devices in the Internet of Things, crowd sensing has emerged as a new way of collecting information from the physical world. Recently, a large variety of crowd sensing systems have been developed, serving a wide spectrum of applications that have significant impact on our daily lives, including urban sensing, smart transportation, environment monitoring, localization, health-care, and many others. However, the sensory data provided by the participating users are usually not reliable, due to various reasons such as poor sensor quality, incomplete observations, and background noise. To identify truthful values from the crowd sensing data, a lot of reliability-aware data aggregation mechanisms have been developed and they can automatically capture user reliability in the data aggregation process. Though able to improve the aggregation accuracy, existing reliability-aware data aggregation mechanisms fail to take into consideration the privacy and security issues in their design. On one hand, the sensory data provided by each individual user may contain sensitive information, which may be disclosed to others during the data aggregation process, resulting in the leakage of users' privacy. On the other hand, there may exist malicious users in crowd sensing systems who conduct the data poisoning attacks for the purpose of sabotage or financial rewards, and the effectiveness of the crowd sensing systems can be largely degraded by these malicious users. In this thesis, we take steps to study and address the privacy and security issues when conducting reliability-aware data aggregation in crowd sensing systems. Specifically, we first consider a widely adopted reliability-aware data aggregation mechanism named truth discovery and propose a series of privacy-preserving truth discovery frameworks for crowd sensing systems. These frameworks can not only accurately calculate the final aggregated results but also provide strong privacy protection for the users' sensitive information. Then, we investigate crowd sensing in adversarial environments and study the data poisoning attacks against the crowd sensing systems employing the truth discovery mechanism. We develop an optimal attack framework in which the attacker can not only maximize his attack utility but also disguise the introduced malicious users as normal ones such that they cannot be detected easily. Following a similar design philosophy, we also successfully attack the crowd sensing systems empowered with the Dawid-Skene model, another widely adopted reliability-aware data aggregation algorithm. The desirable performance of the proposed frameworks is verified through extensive experiments conducted on real-world crowd sensing systems.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards Privacy-Preserving and Secure Crowd Sensing in the Internet of Things"]}]}],"canonical_facts":{"dc:contributor":["Su, Lu","Computer Science and Engineering"],"dc:creator":["Miao, Chenglin"],"dc:date":["2025-02-21T21:35:45Z","2020"],"dc:description":["Ph.D.","Recent years have witnessed the rise of Internet of Things (IoT), a newly emergent networking paradigm that connects humans and the physical-world through ubiquitous sensing, computing, and communicating devices. Driven by the ubiquitous and interconnected sensing devices in the Internet of Things, crowd sensing has emerged as a new way of collecting information from the physical world. Recently, a large variety of crowd sensing systems have been developed, serving a wide spectrum of applications that have significant impact on our daily lives, including urban sensing, smart transportation, environment monitoring, localization, health-care, and many others. However, the sensory data provided by the participating users are usually not reliable, due to various reasons such as poor sensor quality, incomplete observations, and background noise. To identify truthful values from the crowd sensing data, a lot of reliability-aware data aggregation mechanisms have been developed and they can automatically capture user reliability in the data aggregation process. Though able to improve the aggregation accuracy, existing reliability-aware data aggregation mechanisms fail to take into consideration the privacy and security issues in their design. On one hand, the sensory data provided by each individual user may contain sensitive information, which may be disclosed to others during the data aggregation process, resulting in the leakage of users' privacy. On the other hand, there may exist malicious users in crowd sensing systems who conduct the data poisoning attacks for the purpose of sabotage or financial rewards, and the effectiveness of the crowd sensing systems can be largely degraded by these malicious users. In this thesis, we take steps to study and address the privacy and security issues when conducting reliability-aware data aggregation in crowd sensing systems. Specifically, we first consider a widely adopted reliability-aware data aggregation mechanism named truth discovery and propose a series of privacy-preserving truth discovery frameworks for crowd sensing systems. These frameworks can not only accurately calculate the final aggregated results but also provide strong privacy protection for the users' sensitive information. Then, we investigate crowd sensing in adversarial environments and study the data poisoning attacks against the crowd sensing systems employing the truth discovery mechanism. We develop an optimal attack framework in which the attacker can not only maximize his attack utility but also disguise the introduced malicious users as normal ones such that they cannot be detected easily. Following a similar design philosophy, we also successfully attack the crowd sensing systems empowered with the Dawid-Skene model, another widely adopted reliability-aware data aggregation algorithm. The desirable performance of the proposed frameworks is verified through extensive experiments conducted on real-world crowd sensing systems.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86625"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["computer science","computer engineering"],"dc:title":["Towards Privacy-Preserving and Secure Crowd Sensing in the Internet of Things"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:32Z"}