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Youliang Tian
Researcher at Guizhou University
Publications - 91
Citations - 1617
Youliang Tian is an academic researcher from Guizhou University. The author has contributed to research in topics: Computer science & Game theory. The author has an hindex of 16, co-authored 63 publications receiving 901 citations. Previous affiliations of Youliang Tian include Xidian University & Chinese Academy of Sciences.
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An efficient two-factor user authentication scheme with unlinkability for wireless sensor networks
TL;DR: The proposed enhanced authentication scheme with unlinkability not only remedies its security flaws but also improves its performance and is more suitable for practical applications of WSNs than Xue et al.
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An untraceable temporal-credential-based two-factor authentication scheme using ECC for wireless sensor networks
TL;DR: An untraceable two-factor authentication scheme based on elliptic curve cryptography (ECC) for WSNs that makes up for the missing security features necessary for real-life applications while maintaining the desired features of the original scheme.
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A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT
TL;DR: A personalized privacy protection (PERIO) framework based on game theory and data encryption, which is then combined with game theory to construct a rational uploading strategy and a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness.
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A Blockchain-Based Secure Key Management Scheme With Trustworthiness in DWSNs
TL;DR: A blockchain-based secure key management scheme (BC-EKM) is proposed, where stake blockchain as a trust machine replaces the majority functions of the BS and a secure cluster formation and secure node movement algorithm are designed to implement key management.
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An AI-Enabled Three-Party Game Framework for Guaranteed Data Privacy in Mobile Edge Crowdsensing of IoT
TL;DR: An artificial intelligence (AI)-enabled three-party game (ATG) framework for guaranteed data privacy in the MECS of IoT is proposed, based on the random forest classifier and the $k$-anonymity algorithm, that effectively guarantees the privacy of sensitive data.