blog.cs.ut.ee/2023/05/29/mohammad-anagreh-privacy-preserving-parallel-computations-for-graph-problems
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Mohammad Anagreh: “Privacy-preserving parallel computations for graph problems” - UniTartuCS blog
Introduction: Privacy-preserving computation is a crucial discipline that protects the privacy of confidential data while facilitating secure analysis. In graph problems, secure multiparty computation (SMC) presents a promising approach to performing these functionalities. This article delves into the notable advancements in privacy-preserving parallel algorithms recently introduced in a Ph.D. thesis. By integrating graph algorithms with
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Mohammad Anagreh: “Privacy-preserving parallel computations for graph problems” - UniTartuCS blog
Introduction: Privacy-preserving computation is a crucial discipline that protects the privacy of confidential data while facilitating secure analysis. In graph problems, secure multiparty computation (SMC) presents a promising approach to performing these functionalities. This article delves into the notable advancements in privacy-preserving parallel algorithms recently introduced in a Ph.D. thesis. By integrating graph algorithms with
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https://blog.cs.ut.ee/2023/05/29/mohammad-anagreh-privacy-preserving-parallel-computations-for-graph-problemsMohammad Anagreh: “Privacy-preserving parallel computations for graph problems” - UniTartuCS blog
Introduction: Privacy-preserving computation is a crucial discipline that protects the privacy of confidential data while facilitating secure analysis. In graph problems, secure multiparty computation (SMC) presents a promising approach to performing these functionalities. This article delves into the notable advancements in privacy-preserving parallel algorithms recently introduced in a Ph.D. thesis. By integrating graph algorithms with
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