International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
Privacy-Preserving Machine Learning Techniques, Challenges And Research Directions Deval Parikh1, Sarangkumar Radadia2, Raghavendra Kamarthi Eranna3 1Sr Software Engineer 2Principal/Associate Dir Software Development/ Engineering 3GCP Cloud Engineer
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - As machine learning models become increasingly ubiquitous, ensuring privacy protection has emerged as a critical concern. This paper presents an in-depth exploration of privacy-preserving machine learning (PPML) techniques, challenges, and future research directions. We delve into the complexities of integrating privacy-preserving methodologies into machine learning algorithms, pipelines, and architectures. Our review highlights the evolving landscape of regulatory frameworks and the pressing need for innovative solutions to mitigate privacy risks. Moreover, we propose a comprehensive framework, the Phase, Guarantee, and Utility (PGU) model, to systematically evaluate PPML solutions, providing a roadmap for researchers and practitioners. By fostering interdisciplinary collaboration among the machine learning, distributed systems, security, and privacy communities, this paper aims to accelerate progress in PPML, paving the way for robust and privacy-preserving machine learning systems.
Figure 1.1: Privacy-Preserving Machine Learning [13] Here's a breakdown of your statement with references and citations to support each point: Here's a breakdown of your statement with references and citations to support each point:
Key Words: Machine Learning, Differential Privacy, Federated Learning
a. The Ubiquity of Machine Learning:
1.INTRODUCTION In the era of big data and ubiquitous AI, machine learning (ML) algorithms are increasingly used to extract insights from vast datasets. However, these datasets often contain sensitive information about individuals, organizations, or proprietary business operations. Balancing the power of ML with the critical need to protect this privacy is where Privacy-Preserving Machine Learning (PPML) comes in.
A 2022 McKinsey Global Survey found that 83% of respondents believe AI will be as important to their industries as the internet [1].
ML applications span various domains, including:
1.1 Why is PPML important? Machine learning has revolutionized diverse fields, from healthcare and finance to marketing and social media. However, its reliance on vast datasets often containing sensitive information raises crucial privacy concerns. Organizations face growing pressure to navigate a complex landscape of privacy regulations and ethical considerations while reaping the benefits of ML. This is where privacypreserving machine learning (PPML) plays a vital role. Privacy-Preserving Machine Learning is a step-by-step approach to preventing data leakage in machine learning algorithms. PPML allows many privacy-enhancing strategies to allow multiple input sources to train ML models cooperatively without exposing their private data in its original form.
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Impact Factor value: 8.226
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Healthcare: Predicting disease outbreaks, analyzing medical images, and personalizing treatment plans [2].
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Finance: Fraud detection, credit risk assessment, and algorithmic trading [3].
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Marketing: Personalized recommendations, targeted advertising, and customer segmentation [4].
b. Privacy Risks Associated with ML:
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Large-scale data collection practices can lead to: o
Exposure of sensitive personal information: Health records, financial data, and browsing history [5].
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Algorithmic bias and discrimination: Models trained on biased data can perpetuate unfair outcomes [6].
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