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Wei Jiang
Researcher at Missouri University of Science and Technology
Publications - 104
Citations - 3839
Wei Jiang is an academic researcher from Missouri University of Science and Technology. The author has contributed to research in topics: Information privacy & Encryption. The author has an hindex of 26, co-authored 99 publications receiving 3202 citations. Previous affiliations of Wei Jiang include University of Missouri & Xidian University.
Papers
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Journal ArticleDOI
Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control
TL;DR: An App (called Healthcare Data Gateway (HGD) architecture based on blockchain is proposed to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private.
Proceedings ArticleDOI
Secure k-nearest neighbor query over encrypted data in outsourced environments
TL;DR: Wang et al. as discussed by the authors proposed a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns, and empirically analyzed the efficiency of their protocols through various experiments.
Posted Content
Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments
TL;DR: This paper focuses on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user.
Journal ArticleDOI
A secure distributed framework for achieving k -anonymity
Wei Jiang,Chris Clifton +1 more
TL;DR: A two-party framework along with an application that generates k-anonymous data from two vertically partitioned sources without disclosing data from one site to the other satisfies the secure definition commonly defined in the literature of Secure Multiparty Computation.
Journal ArticleDOI
k -Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data
TL;DR: This work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model and empirically analyzes the efficiency of the proposed protocol using a real-world dataset under different parameter settings.