G
Gaby G. Dagher
Researcher at Boise State University
Publications - 24
Citations - 823
Gaby G. Dagher is an academic researcher from Boise State University. The author has contributed to research in topics: Information sensitivity & Solvency. The author has an hindex of 8, co-authored 24 publications receiving 525 citations. Previous affiliations of Gaby G. Dagher include Concordia University Wisconsin & Concordia University.
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Journal ArticleDOI
Ancile: Privacy-Preserving Framework for Access Control and Interoperability of Electronic Health Records Using Blockchain Technology
TL;DR: A blockchain-based framework for secure, interoperable, and efficient access to medical records by patients, providers, and third parties, while preserving the privacy of patients’ sensitive information is proposed, named Ancile.
Proceedings ArticleDOI
BroncoVote: Secure Voting System Using Ethereum’s Blockchain
TL;DR: This paper proposes a blockchain-based voting system, named BroncoVote, that preserves voter privacy and increases accessibility, while keeping the voting system transparent, secure, and cost-effective.
Proceedings ArticleDOI
Provisions: Privacy-preserving Proofs of Solvency for Bitcoin Exchanges
TL;DR: Provisions is introduced, a privacy-preserving proof of solvency whereby an exchange does not have to disclose its Bitcoin addresses; total holdings or liabilities; or any information about its customers; or an extension which prevents exchanges from colluding to cover for each other's losses.
Journal ArticleDOI
SafePath: Differentially-private publishing of passenger trajectories in transportation systems
TL;DR: This paper study the problem of privacy-preserving passenger trajectories publishing and propose a solution under the rigorous differential privacy model, called SafePath, that models trajectories as a noisy prefix tree and publishes ϵ-differentially-private trajectories while minimizing the impact on data utility.
Journal ArticleDOI
Subject-based semantic document clustering for digital forensic investigations
TL;DR: A new subject-based semantic document clustering model is proposed that allows an investigator to cluster documents stored on a suspect's computer by grouping them into a set of overlapping clusters, each corresponding to a subject of interest initially defined by the investigator.