Federated Learning in Privacy-Preserving Machine Learning - Balancing Model Accuracy and Data Security - Research Article

Aug 4, 2026

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The research explores Privacy-Preserving Machine Learning (PPML) methods, focusing on Differential Privacy, Federated Learning, and Secure Multi-Party Computation, to address the tension between data confidentiality and predictive performance. Privacy models are assessed for achieving substantial privacy protection while limiting accuracy degradation. The study emphasizes practical value across domains such as Healthcare, Banking, Internet of Things (IoT), and Manufacturing, reporting enhanced data security, high model accuracy, and directions for future development through improved evaluation ideas.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Literature Review

Privacy-Preserving Machine Learning Techniques and its Challenges

Illustration, Balances and Methods Related to Privacy-Preserving Architectures

A significant review regarding the privacypreserving techniques for deep learning

Federated Learning in Privacy-Preserving Machine Learning: Balancing Model Accuracy and Data Security Rajeev Agarwal Assistant Professor, Amity University, Pune, INDIA. [www.jrasb.com || Vol. 2 No. 4](www.jrasb.com || Vol. 2 No. 4) (2023): August Issue Received: 21-07-2023 Revised: 01-08-2023 Accepted: 18-08-2023 ABSTRACT This research article provides information regarding the concerns of Privacy-Preserving Machine Learning (PPML) Techniques that includes Differential Privacy, Federated Learning, and Secure Multi-Party Computation. It is also noticed that Privacy Models are useful to achieve significant privacy and minimal loss in model accuracy. This further demonstrates that these kinds of strategies in the main application areas such as Healthcare, Banking, Internet-Of-Things (IOT), and Manufacturing, provides near-perfect privacy that can be useful while it meets minimal compromise in model accuracy. Some of the findings are enhanced data security, high accuracy of the chosen model, and ideas for future development. Keywords-Differential Privacy, Federated Learning, Secure Multi-Party Computation, Banking. I. INTRODUCTION In the present scenarios it is noticed that there are specific data-driven solutions, it becomes important to choose between using data and maintaining its privacy because using machine learning (ML) as one of the tools of enterprise intelligence is becoming a trend. To address this problem, Privacy-Preserving Machine Learning solutions have been developed as a way of ensuring that data that is used in the ML models does not compromise privacy. Several approaches like Differential privacy, Federated learning, and Secure multi-party computation focus at preserving the data and at the same time do not compromise on the model quality. Assessing several specific methods critically, will provide better understanding of the ways to achieve the best compromise between data protection and the efficiency of the learning machines. II. LITERATURE REVIEW 2.1 Privacy-Preserving Machine Learning Techniques and its Challenges As the use of machine learning models increases, the issue of privacy increases as well. This research aim was to have a closer look at Privacy- Preserving Machine Learning (PPML) algorithms together with significant challenges and potential avenues for further research. Some important areas that were addressed are, how privacy-preserving methodologies are applicable to more general problems in machine learning, algorithms, pipelines, and structures, especially given the constantly shifting legal landscape. The focus of the research was to increase the area of PPML by providing the improved Phase, Guarantee, and Utility (PGU) model. This technique provides a structured approach to systematically assess the PPML solutions that is beneficial for the guiding map for the researchers. It involves literature analysis and generation of the PGU model. PPML obtained results that include a broadened understanding of PPML techniques, it sets a new set of evaluation criteria, and the definition of important research directions. Future work will proceed to enhance the PGU model and analyze the future privacy technologies and allied domains for the remaining privacy challenges for ML. Figure 1: Privacy-Preserving Machine Learning (Source: [https://www.researchgate.net](https://www.researchgate.net)) 2.2 Illustration, Balances and Methods Related to Privacy-Preserving Architectures As the artificial intelligence and blockchain grows and develops gradually, privacy is far more significant. This research provided a general background of AI as well as blockchain with a focus on synergy, development of privacy protection solutions. This research analyzed several application areas such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity solutions. The goal was to critically assess five key aspects of privacy protection systems in AI-blockchain integra

FAQs

Which PPML techniques are discussed in the article?

The article discusses Differential Privacy, Federated Learning, and Secure Multi-Party Computation as core Privacy-Preserving Machine Learning techniques.

How does the article address the trade-off between privacy and model accuracy?

It describes privacy models and PPML strategies as ways to achieve significant privacy protection while minimizing loss in model accuracy and preserving overall model quality.

What application areas are highlighted for privacy-preserving machine learning?

The article highlights Healthcare, Banking, Internet of Things (IoT), and Manufacturing as main application areas where privacy protection can be achieved with limited compromise in accuracy.

Aug 4, 2026

Upload by Asher