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What is Secure Multi-Party Computation (SMPC)

Chapter 2
Developments Towards Next Generation Intelligent Systems for Sustainable Development
Secure Multi-Party Computation is a cryptographic technique that enables multiple parties to jointly compute a function over their inputs while keeping those inputs private. It ensures that no party learns the private inputs of others while obtaining the desired computation results. SMPC is used to perform collaborative computations securely in scenarios where data privacy is a concern.
Published in Chapter:
Security and Privacy Considerations in Cloud-Based Data Processing Solutions for Sensitive Data
Tarun Kumar Vashishth (IIMT University, India), Vikas Sharma (IIMT University, India), Kewal Krishan Sharma (IIMT University, India), Bhupendra Kumar (IIMT University, India), Sachin Chaudhary (IIMT University, India), and Rajneesh Panwar (IIMT University, India)
Copyright: © 2024 |Pages: 27
DOI: 10.4018/979-8-3693-5643-2.ch002
Abstract
This book chapter explores the crucial aspects of security and privacy considerations in cloud-based data processing solutions for sensitive data. As organizations increasingly leverage cloud computing for their data processing needs, concerns regarding the protection of sensitive information have become paramount. The chapter discusses the challenges and potential threats associated with cloud-based data processing, highlighting the importance of implementing robust security measures to safeguard sensitive data. The chapter delves into various security and privacy considerations that must be addressed when adopting cloud-based data processing solutions. It covers topics such as data encryption, access control mechanisms, secure data transmission, and secure storage. Additionally, it examines the role of authentication and authorization mechanisms, as well as the importance of auditing and monitoring activities to ensure compliance with data protection regulations.
Full Text Chapter Download: US $37.50 Add to Cart
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Chapter 9
Federated Learning for Privacy Preservation in Healthcare: A Comprehensive Introduction
Cryptographic methods enabling collaborative model updates without exposing individual data in federated learning scenarios.
Published in Chapter: Federated Learning for Privacy Preservation in Healthcare: A Comprehensive Introduction; From: Federated Learning and Privacy-Preserving in Healthcare AI
Full Text Chapter Download: US $37.50 Add to Cart
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