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Privacy-preserving Parallel Computation of Shortest Path ...
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由 M Anagreh 著作2022被引用 6 次 — The privacy-preserving Bellman-Ford and. Dijkstra SSSD algorithms are secure with low round complexity but are not the lowest round complexity than our work. 3 ...
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Privacy-preserving Parallel Computation of Shortest Path ...
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In this paper, we present a secure multiparty computation (SMC) protocol for single-source shortest distances (SSSD) in undirected graphs, where the location of ...
Parallel Privacy-Preserving Shortest Path Algorithms
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由 M Anagreh 著作2021被引用 13 次 — In this paper, we propose and present secure multiparty computation (SMC) protocols for single-source shortest distance (SSSD) and all-pairs shortest distance ...
(PDF) Privacy-preserving Parallel Computation of Shortest Path ...
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This paper studies the secure multiparty computation (SMC) protocol for the shortest path problem in sparse and dense graphs, building upon the breadth-first ...
Privacy-preserving Parallel Computation of Shortest Path ...
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This paper studies the secure multiparty computation (SMC) protocol for the shortest path problem in sparse and dense graphs, building upon the breadth-first ...
[PDF] Parallel Privacy-Preserving Shortest Path Algorithms
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These protocols follow the structure of classical algorithms—Bellman–Ford and Dijkstra for SSSD; Johnson, Floyd–Warshall, and transitive closure for APSD; ...
Mohammad Anagreh
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SN Computer Science 3 (448), 2022. 5, 2022. Privacy-preserving Parallel Computation of Shortest Path Algorithms with Low Round Complexity. M Anagreh, P Laud, E ...
A Parallel Privacy-Preserving Shortest Path Protocol from a ...
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由 M Anagreh 著作2022被引用 4 次 — In this paper, we present a secure multiparty computation (SMC) protocol for single-source shortest distances (SSSD) in undirected graphs, ...
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Privacy-preserving Parallel Computation of Shortest Path Algorithms with Low Round Complexity ; Pages. 37–47 ; ISBN. 978-989-758-553-1 ; Status. Published ; DOI.
A Parallel Privacy-Preserving Shortest Path Protocol from a ...
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由 M Anagreh 著作被引用 4 次 — Privacy-preserving parallel algorithms are needed to expedite the processing of large private data sets for graph algorithms and meet high-end computational ...