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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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Proceedings ArticleDOI
Device-invisible two-factor authenticated key agreement protocol for BYOD
TL;DR: A device-invisible two-factor authenticated key agreement protocol (Dtaka) based on identity-based and password-based authentications to protect the corporate data and simplify the device management for BYOD is proposed.
Proceedings ArticleDOI
Fine-Grained Identification with Real-Time Fairness in Mobile Social Networks
TL;DR: This paper proposes a novel fine-grained identification protocol, which provides confidentiality, unlinkability, and real-time fairness without the involvement of TTP, and demonstrates that fairness can be well guaranteed as long as users strictly follow the protocol rules.
Book ChapterDOI
Enabling Efficient and Fine-Grained DNA Similarity Search with Access Control over Encrypted Cloud Data
TL;DR: This paper creatively put forward a private edit distance approximation algorithm to realize the efficient and high accurate DNA similarity query and proposes an Efficient DNA Similarity Search scheme (EDSS) which can achieve fine-grained query and data access control over encrypted cloud data.
Proceedings ArticleDOI
A Deep Learning Framework Supporting Model Ownership Protection and Traitor Tracing
TL;DR: SecureMark_DL as mentioned in this paper enables a model owner to embed a unique fingerprint for every customer within parameters of a DL model, extract and verify the fingerprint from a pirated model, and hence trace the rogue customer who illegally distributed his model for profits.
Proceedings ArticleDOI
Efficient and Privacy-Preserving Ad Conversion for V2X-Assisted Proximity Marketing
TL;DR: This paper designs a novel and efficient PSI scheme that is secure in the presence of malicious adversaries, and proposes a privacy-preserving ad conversion protocol for V2X-assisted proximity marketing, that can achieve input privacy, unlinkability, unforgeability, and output verifiability.