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What is Homomorphic Encryption

Chapter 9
Federated Learning and Privacy-Preserving in Healthcare AI
A method that enables computations on encrypted data, enhancing the privacy of model aggregation in federated learning.
Published in Chapter:
Federated Learning for Privacy Preservation in Healthcare: A Comprehensive Introduction
Hari Kishan Kondaveeti (Vellore Institute of Technology, India), Chinna Gopi Simhadri (Vellore Institute of Technology, India), Srileakhana Mangapathi (Vellore Institute of Technology, India), and Valli Kumari Vatsavayi (Andhra University, India)
Copyright: © 2024 |Pages: 16
DOI: 10.4018/979-8-3693-1874-4.ch009
Abstract
This chapter delves into fundamental concepts of privacy preservation and federated learning (FL) in healthcare. Emphasizing the importance of privacy in healthcare data, it explores ethical and regulatory considerations surrounding sensitive patient information. The history and significance of FL, distinct from traditional centralized machine learning, are discussed, highlighting its relevance in addressing privacy concerns. The limitations of centralized ML are contrasted with FL's advantages, particularly in preserving privacy. Techniques such as FL averaging, aggregation, and secure multi-party computation (SMPC) for privacy-preserving model updates are examined. Real-world examples illustrate their application in healthcare scenarios. The chapter concludes by addressing technical and ethical challenges linked to FL in healthcare, emphasizing its potential to balance patient data protection with AI advancements. Privacy concerns persist in healthcare AI, making FL a promising solution. The discussion extends to emerging trends and potential breakthroughs in this dynamic field.
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More Results
Chapter 19
Medical Data Analytics in the Cloud Using Homomorphic Encryption
An encryption system capable of performing meaningful operations on the encrypted messages without accessing the original message.
Published in Chapter: Medical Data Analytics in the Cloud Using Homomorphic Encryption; From: Handbook of Research on Cloud Infrastructures for Big Data Analytics
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Chapter 10
Suggesting New Techniques and Methods for Big Data Analysis: Privacy-Preserving Data Analysis Techniques
This advanced encryption method allows data to remain encrypted during processing, supporting computations on the encrypted data to generate encrypted results. When these results are decrypted, they match the outcome of operations as if they were performed on the original, unencrypted data, offering high data security during processing.
Published in Chapter: Suggesting New Techniques and Methods for Big Data Analysis: Privacy-Preserving Data Analysis Techniques; From: Big Data Analytics Techniques for Market Intelligence
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Chapter 10
Secure Data Deduplication of Encrypted Data in Cloud
A cryptographic method that allows performing mathematical calculations on encrypted information (cipher text) without decrypting it first.
Published in Chapter: Secure Data Deduplication of Encrypted Data in Cloud; From: Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization
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Chapter 9
Security Mechanisms in Cloud Computing-Based Big Data
It is an encryption mechanism which involves computations being done on ciphered information. This mechanism supports in processing the data before encryption it which makes it more secure. Thus, this system avoids the limitation of most security systems (i.e., the necessity to decrypt data before processing it). The RSA algorithm is used in this environment for ciphered data multiplication and ECC algorithm depending on its construction can be used for ciphered data addition process.
Published in Chapter: Security Mechanisms in Cloud Computing-Based Big Data; From: Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 3
A Blockchain-Based Approach to Revolutionizing Healthcare
It is a type of encryption where data that has been encoded can be analyzed without the information having to be decrypted. Data confidentiality is essential in many fields, making homomorphic encryption a useful tool for handling information with data protection considerations and numerous regulations. Models infer from encrypted info, so they cannot access private client information. This prevents data from being compromised or exposed. For any computation, the end user and the model operator need not communicate.
Published in Chapter: A Blockchain-Based Approach to Revolutionizing Healthcare; From: Revolutionizing Digital Healthcare Through Blockchain Technology Applications
Full Text Chapter Download: US $37.50 Add to Cart
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