Abdur Rahman Bin Shahid
School of Computing and Information Sciences
Abdur Rahman Bin Shahid is a Ph.D. candidate at Florida International University’s School of Computing and Information Sciences (SCIS) under the supervision of Dr. Niki Pissinou. He received his B.Sc. in Computer Science and Engineering in December 2011 from Chittagong University of Engineering and Technology, Bangladesh. He worked as a Software Engineer at Samsung Bangladesh R&D Center and Samsung Electronics for two years before joining the SCIS Ph.D. program. His research interests include privacy, security, Internet of Things (IoT), blockchain, and mobile computing. His current research focuses on location privacy preservation and lightweight blockchain development.
Various research efforts have been undertaken to solve the problem of trajectory privacy preservation in the Internet of Things (IoT) of resource-constrained mobile devices. Most attempts at resolving the problem have focused on the centralized model of IoT, which either impose high delay or fail against a privacy-invading attack with long-term trajectory observation. These proposed solutions also fail to guarantee location privacy for trajectories with both geo-tagged and non-geo-tagged data, since they are designed for geo-tagged trajectories only. While a few blockchain-based techniques have been suggested for preserving trajectory privacy in decentralized model of IoT, they require large storage capacity on resource-constrained devices and can only provide conditional privacy when a set of authorities governs the blockchain. This dissertation addresses these challenges to develop efficient trajectory privacy-preservation and lightweight blockchain techniques for mobility-centric IoT.
We develop a pruning-based technique by quantifying the relationship between trajectory privacy and delay for real-time geo-tagged queries. This technique yields higher trajectory privacy with a reduced delay than contemporary techniques while preventing a long-term observation attack. We extend our study with the consideration of the presence of non-geo-tagged data in a trajectory. We design an attack model to show the spatiotemporal correlation between the geo-tagged and non-geo-tagged data which undermines the privacy guarantee of existing techniques. In response, we propose a methodology that considers the spatial distribution of the data in trajectory privacy-preservation and improves existing solutions, in privacy and usability.
With respect to blockchain, we design and implement one of the first blockchain storage management techniques utilizing the mobility of the devices. This technique reduces the required storage space of a blockchain and makes it lightweight for resource-constrained mobile devices. To address the trajectory privacy challenges in an authority-based blockchain under the short-range communication constraints of the devices, we introduce a silence-based one of the first technique to establish a balance between trajectory privacy and blockchain utility.
The designed trajectory privacy-preservation techniques we established are lightweight and do not require an intermediary to guarantee trajectory privacy, thereby providing practical and efficient solution for different mobility-centric IoT, such as mobile crowdsensing and Internet of Vehicles.