Secure Multiparty Computation for Machine Learning in Healthcare

Authors

  • Venkata Raju, Sirisha Balla, Selvaraj Sakthivel

Keywords:

cryptographic, collaboratively, stakeholders, emphasizing.

Abstract

A robust cryptographic framework known as Secure Multi-Party Computation (SMPC) been developed, enabling several participants to collaboratively perform data analysis tasks while preserving the confidentiality and privacy of their own data. Collaborative data analysis is becoming prevalent in several domains, such as healthcare, finance, and social sciences, where multiple stakeholders need to share and assess sensitive information without revealing it to external parties. This paper provides a comprehensive examination of SMPC for collective data analysis. The primary objective is to provide a comprehensive overview of the SMPC's foundational principles, protocols, and applications, while emphasizing the benefits and challenges it poses for facilitating secure collaboration across diverse data proprietors. This paper provides a comprehensive and up-to-date analysis of Secure Multi-Party Computation for collaborative data analysis. It offers a comprehensive understanding of the challenges associated with SMPC implementation, along with the fundamental concepts, protocols, and applications. The paper aims to serve as a valuable resource for academics, professionals, and decision-makers seeking to use Secure Multi-Party Computation (SMPC) for collaborative data analysis while ensuring secrecy and privacy.

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Published

30.10.2024

How to Cite

Venkata Raju. (2024). Secure Multiparty Computation for Machine Learning in Healthcare. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 5653 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7506

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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