L
Linke Guo
Researcher at Clemson University
Publications - 65
Citations - 1565
Linke Guo is an academic researcher from Clemson University. The author has contributed to research in topics: Information privacy & Computer science. The author has an hindex of 19, co-authored 55 publications receiving 1163 citations. Previous affiliations of Linke Guo include Binghamton University & Xidian University.
Papers
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Proceedings ArticleDOI
A game-theoretic approach for achieving k-anonymity in Location Based Services
TL;DR: This paper model the distributed dummy user generation as Bayesian games in both static and timing-aware contexts, and analyze the existence and properties of the Bayesian Nash Equilibria for both models, and proposes a strategy selection algorithm to help users achieve optimized payoffs.
Journal ArticleDOI
A Privacy-Preserving Attribute-Based Authentication System for Mobile Health Networks
TL;DR: This work proposes a decentralized system that leverages users' verifiable attributes to authenticate each other while preserving attribute and identity privacy, and designs authentication strategies with progressive privacy requirements in different interactions among participating entities.
Proceedings ArticleDOI
PAAS: A Privacy-Preserving Attribute-Based Authentication System for eHealth Networks
TL;DR: A framework called PAAS is proposed which leverages users' verifiable attributes to authenticate users in eHealth systems while preserving their privacy issues and is shown to be better than existing e health systems in terms of privacy preservation and practicality.
Journal ArticleDOI
A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks
Linke Guo,Chi Zhang,Yuguang Fang +2 more
TL;DR: The feasibility and privacy preservation of the proposed trust-based privacy-preserving friend recommendation scheme for OSNs is shown, where OSN users apply their attributes to find matched friends, and establish social relationships with strangers via a multi-hop trust chain.
Proceedings ArticleDOI
Privacy-Preserving Machine Learning Algorithms for Big Data Systems
TL;DR: This paper utilizes the data locality property of Apache Hadoop architecture and only a limited number of cryptographic operations at the Reduce() procedures to achieve privacy-preservation in a novel framework where the training data are distributed and each shared data portion is of large volume.