Privacy Preserving Machine Learning
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Privacy Preserving Machine Learning
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Author : Jin Li
language : en
Publisher: Springer Nature
Release Date : 2022-03-14
Privacy Preserving Machine Learning written by Jin Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-14 with Computers categories.
This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.
Privacy Preserving Machine Learning
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Author : J. Morris Chang
language : en
Publisher: Simon and Schuster
Release Date : 2023-05-23
Privacy Preserving Machine Learning written by J. Morris Chang and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-23 with Computers categories.
Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy-Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You’ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. By the time you’re done reading, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance. About the Technology Machine learning applications need massive amounts of data. It’s up to you to keep the sensitive information in those data sets private and secure. Privacy preservation happens at every point in the ML process, from data collection and ingestion to model development and deployment. This practical book teaches you the skills you’ll need to secure your data pipelines end to end. About the Book Privacy-Preserving Machine Learning explores privacy preservation techniques through real-world use cases in facial recognition, cloud data storage, and more. You’ll learn about practical implementations you can deploy now, future privacy challenges, and how to adapt existing technologies to your needs. Your new skills build towards a complete security data platform project you’ll develop in the final chapter. What’s Inside Differential and compressive privacy techniques Privacy for frequency or mean estimation, naive Bayes classifier, and deep learning Privacy-preserving synthetic data generation Enhanced privacy for data mining and database applications About the Reader For machine learning engineers and developers. Examples in Python and Java. About the Author J. Morris Chang is a professor at the University of South Florida. His research projects have been funded by DARPA and the DoD. Di Zhuang is a security engineer at Snap Inc. Dumindu Samaraweera is an assistant research professor at the University of South Florida. The technical editor for this book, Wilko Henecka, is a senior software engineer at Ambiata where he builds privacy-preserving software. Table of Contents PART 1 - BASICS OF PRIVACY-PRESERVING MACHINE LEARNING WITH DIFFERENTIAL PRIVACY 1 Privacy considerations in machine learning 2 Differential privacy for machine learning 3 Advanced concepts of differential privacy for machine learning PART 2 - LOCAL DIFFERENTIAL PRIVACY AND SYNTHETIC DATA GENERATION 4 Local differential privacy for machine learning 5 Advanced LDP mechanisms for machine learning 6 Privacy-preserving synthetic data generation PART 3 - BUILDING PRIVACY-ASSURED MACHINE LEARNING APPLICATIONS 7 Privacy-preserving data mining techniques 8 Privacy-preserving data management and operations 9 Compressive privacy for machine learning 10 Putting it all together: Designing a privacy-enhanced platform (DataHub)
Privacy Preserving Deep Learning
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Author : Kwangjo Kim
language : en
Publisher: Springer Nature
Release Date : 2021-07-22
Privacy Preserving Deep Learning written by Kwangjo Kim and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-22 with Computers categories.
This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google’s infamous announcement of “Private Join and Compute,” an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world. This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.
Privacy Preserving Machine Learning
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Author : Srinivasa Rao Aravilli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-24
Privacy Preserving Machine Learning written by Srinivasa Rao Aravilli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-24 with Computers categories.
Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches Key Features Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches Develop and deploy privacy-preserving ML pipelines using open-source frameworks Gain insights into confidential computing and its role in countering memory-based data attacks Purchase of the print or Kindle book includes a free PDF eBook Book Description– In an era of evolving privacy regulations, compliance is mandatory for every enterprise – Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information – This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases – As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy – Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models – You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field – Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks What you will learn Study data privacy, threats, and attacks across different machine learning phases Explore Uber and Apple cases for applying differential privacy and enhancing data security Discover IID and non-IID data sets as well as data categories Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks Understand secure multiparty computation with PSI for large data Get up to speed with confidential computation and find out how it helps data in memory attacks Who this book is for – This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers – Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn) – Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques
Privacy Preserving Machine Learning Over Distributed Data
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Author : Ali Reza Ghavamipour
language : en
Publisher:
Release Date : 2024
Privacy Preserving Machine Learning Over Distributed Data written by Ali Reza Ghavamipour and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Towards Effective Efficient And Equitable Privacy Preserving Machine Learning
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Author : Nitin Agrawal
language : en
Publisher:
Release Date : 2021
Towards Effective Efficient And Equitable Privacy Preserving Machine Learning written by Nitin Agrawal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Privacy Preserving Machine Learning
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Author : Srinivasa Rao Aravilli
language : en
Publisher: Packt Publishing
Release Date : 2023-08
Privacy Preserving Machine Learning written by Srinivasa Rao Aravilli and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08 with Computers categories.
This book helps software engineers, data scientists, ML and AI engineers, and research and development teams to learn and implement privacy-preserving machine learning as well as protect companies against privacy breaches.
An Application Of Secure Data Aggregation For Privacy Preserving Machine Learning On Mobile Devices
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Author : Chuyi Liu
language : en
Publisher:
Release Date : 2018
An Application Of Secure Data Aggregation For Privacy Preserving Machine Learning On Mobile Devices written by Chuyi Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Machine learning categories.
Machine learning algorithms over big data have been widely used to make low-priced services better over the years, but they come with privacy as a major public concern. The European Union has made the General Data Protection Regulation (GDPR) enforceable recently, and the GDPR mainly focuses on giving citizens and residents more control over their personal data. On the other hand, with personal and collective data from users, companies can provide better experience for customers like customized news feeds and real time transportation systems. To solve this dilemma, many privacy-preserving schemes have been proposed such as homomorphic encryption and machine learning over encrypted data. However, many of them are not practical for the time being due to the high com- putational complexity. In 2017, Bonawitz et al. proposed a practical scheme for secure data aggregation from privacy-preserving machine learning, which comes with the afford- able calculation and communication complexity that considers practical users' drop-out situations. However, the communication complexity of the scheme is not efficient enough because a mobile user needs to communicate with all the members in the network to es- tablish a secure mutual key with each other. In this thesis, by combining the Harn-Gong key establishment protocol and the mobile data aggregation scheme, we propose an efficient mobile data aggregation protocol with privacy-preserving by introducing a non-interactive key establishment protocol which re- duces the communication complexity for pairwise key establishment of n users from O(n2) to a constant value. We correct the security proof of Harn-Gong key establishment protocol and provide a secure threshold of degree of polynomial according to Byzantine Problem. We implement KDC side Harn-Gong key establishment primitives and prepare a proof-of- concept Android mobile application to test our protocol's running time in masking private data. The result shows that our private data masking time is 1.5 to 3 times faster than the original one.
Decentralised And Privacy Preserving Machine Learning Approach For Distributed Data Resources
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Author : Mona Alkhozae
language : en
Publisher:
Release Date : 2023
Decentralised And Privacy Preserving Machine Learning Approach For Distributed Data Resources written by Mona Alkhozae and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Towards Ethical And Robust Privacy Preserving Machine Learning
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Author : Hui Hu
language : en
Publisher:
Release Date : 2022
Towards Ethical And Robust Privacy Preserving Machine Learning written by Hui Hu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Artificial intelligence categories.
Privacy in machine learning has received tremendous attention in recent years, which mainly involves data privacy and model privacy. Recent studies have revealed numerous privacy attacks and privacy-preserving methodologies, that vary across a broad range of applications. To date, however, there exist few powerful methodologies in addressing privacy-preserving challenges in ethical machine learning and deep learning due to the difficulty of guaranteeing model robustness and privacy-preserving simultaneously. In this dissertation, two critical problems will be investigated and addressed: data privacy-preserving in ethical machine learning, and model privacy-preserving in deep learning under powerful side-channel power attacks. First, we investigate the problem of data privacy-preserving in ethical machine learning with the following two considerations: (1) Users’ privacy (i.e., race, religion, gender, etc.) is severely leaked in ethical machine learning as most existing techniques require full access to sensitive personal data to achieve model fairness. To address this pressing privacy issue, we propose a distributed privacy-preserving fair machine learning mechanism based on random projection theory and multi-party computation. Through rigorous theoretical analysis and comprehensive simulations, we can prove that the proposed mechanism is efficient for privacy-preserving while guaranteeing good model robustness. Further, (2) considering the dependency relation of graph data in ethical machine learning, an individual’s privacy can be leaked due to the sensitive information disclosure of their neighbors. Typically, in a graph neural network, the sensitive information disclosure of non-private users potentially exposes the sensitive information of private users in the same graph owing to the homophily property and message-passing mechanism of graph neural networks. To address this problem, based on disentangled representation learning, we propose a principled privacy-preserving graph neural network model to mitigate individual privacy leakage of private users in a graph, which maintains competitive model accuracy compared with non-private graph neural networks. We verify the effectiveness of the proposed privacy-preserving model through extensive experiments and theoretical analysis. Second, as the disclosure of model privacy can allow adversaries to potentially infer users’ extremely sensitive decisions, further, we study model privacy-preserving in deep learning under side-channel power attacks. Side-channel power attacks are powerful attacks that infer the internal information of a traditional deep neural network (i.e., model privacy), which can be leveraged to infer some important decisions of users. Therefore, with the increasing applications of deep learning, training privacy-preserving deep neural networks under side-channel power attacks is a pressing task. This dissertation proposes an efficient solution for training privacy-preserving deep neural networks to resist powerful side-channel power attacks, which randomly trains multiple independent sub-networks to generate random power traces in the temporal domain. The comprehensive theoretical analysis and experimental results demonstrate the effectiveness of the proposed approach in model privacy-preserving and model robustness under side-channel power attacks.