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Xiaodong Lin
Researcher at University of Guelph
Publications - 337
Citations - 18654
Xiaodong Lin is an academic researcher from University of Guelph. The author has contributed to research in topics: Information privacy & Authentication. The author has an hindex of 61, co-authored 315 publications receiving 15199 citations. Previous affiliations of Xiaodong Lin include University of Ontario Institute of Technology & University of Waterloo.
Papers
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Journal ArticleDOI
Balancing Security and Efficiency for Smart Metering Against Misbehaving Collectors
TL;DR: This paper proposes a novel privacy-preserving smart metering scheme to prevent pollution attacks for the balance of security and efficiency in smart grid, and achieves end-to-end security, data aggregation, and integrity protection against the misbehaving collectors.
Journal ArticleDOI
On Countermeasures of Pilot Spoofing Attack in Massive MIMO Systems: A Double Channel Training Based Approach
TL;DR: This paper evaluates the impact of the PSA on the achievable rate with linear processing, and proposes a double channel training based scheme to combat PSA, and derives a closed-form expression of the minimum mean square error precoding scheme to maximize the minimum achievable secrecy rate.
Proceedings ArticleDOI
PReFilter: An efficient privacy-preserving Relay Filtering scheme for delay tolerant networks
TL;DR: PReFilter is an efficient privacy-preserving relay filter scheme to prevent the relay of encrypted junk information early in sparse delay tolerant networks and is not only effective in the filtering of junk packets but also significantly improve the network performance with the dramatically reduced delivery cost due to the junk packets.
Journal ArticleDOI
Invisible Hand: A Privacy Preserving Mobile Crowd Sensing Framework Based on Economic Models
TL;DR: A framework that enhances the location privacy of MCS applications by reducing the bidding and assignment steps in the MCS cycle is proposed and economic theory is used to help both the service provider and participants to decide their strategies.
Journal ArticleDOI
A Multikernel and Metaheuristic Feature Selection Approach for IoT Malware Threat Hunting in the Edge Layer
Hamed HaddadPajouh,Alireza Mohtadi,Ali Dehghantanaha,Hadis Karimipour,Xiaodong Lin,Kim-Kwang Raymond Choo +5 more
TL;DR: The proposed multikernel support vector machine (SVM) approach outperforms DNNs and fuzzy-based IoT malware hunting techniques, in terms of accuracy, while significantly reducing the computational cost and the training time.