Privacy Preserving Deep Learning
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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 : 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.
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.
Deep Learning With Jax
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Author : Grigory Sapunov
language : en
Publisher: Simon and Schuster
Release Date : 2024-12-03
Deep Learning With Jax written by Grigory Sapunov 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 2024-12-03 with Computers categories.
Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX you will learn how to: • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax • Leverage libraries and modules from the JAX ecosystem Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. About the technology Google’s JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications. About the book Deep Learning with JAX teaches you to build effective neural networks with JAX. In this example-rich book, you’ll discover how JAX’s unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You’ll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX’s functional programming mindset improves composability and parallelization. What's inside • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax About the reader For intermediate Python programmers who are familiar with deep learning. About the author Grigory Sapunov holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning. The technical editor on this book was Nicholas McGreivy. Table of Contents Part 1 1 When and why to use JAX 2 Your first program in JAX Part 2 3 Working with arrays 4 Calculating gradients 5 Compiling your code 6 Vectorizing your code 7 Parallelizing your computations 8 Using tensor sharding 9 Random numbers in JAX 10 Working with pytrees Part 3 11 Higher-level neural network libraries 12 Other members of the JAX ecosystem A Installing JAX B Using Google Colab C Using Google Cloud TPUs D Experimental parallelization
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)
Towards Privacy Preserving Deep Learning For Medical Image Analysis
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Author : Soroosh Tayebi Arasteh
language : en
Publisher:
Release Date : 2024*
Towards Privacy Preserving Deep Learning For Medical Image Analysis written by Soroosh Tayebi Arasteh 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.
The 10th International Conference On Science And Technology Icst
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Author : Ganjar Alfian
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2025-10-13
The 10th International Conference On Science And Technology Icst written by Ganjar Alfian and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-13 with Technology & Engineering categories.
Selected peer-reviewed full text papers from the 10th International Conference on Science and Technology (ICST UGM 2024) Selected peer-reviewed full text papers from the 10th International Conference on Science and Technology (ICST UGM 2024), October 23-24, 2024, Yogyakarta, Indonesia
Towards Efficient And Effective Privacy Preserving Machine Learning
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Author : Lingxiao Wang
language : en
Publisher:
Release Date : 2021
Towards Efficient And Effective Privacy Preserving Machine Learning written by Lingxiao Wang 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.
The past decade has witnessed the fast growth and tremendous success of machine learning. However, recent studies showed that existing machine learning models are vulnerable to privacy attacks, such as membership inference attacks, and thus pose severe threats to personal privacy. Therefore, one of the major challenges in machine learning is to learn effectively from enormous amounts of sensitive data without giving up on privacy. This dissertation summarizes our contributions to the field of privacy-preserving machine learning, i.e., solving machine learning problems with strong privacy and utility guarantees. In the first part of the dissertation, we consider the privacy-preserving sparse learning problem. More specifically, we establish a novel differentially private hard-thresholding method as well as a knowledge-transfer framework for solving the sparse learning problem. We show that our proposed methods are not only efficient but can also achieve improved privacy and utility guarantees. In the second part of the dissertation, we propose novel efficient and effective algorithms for solving empirical risk minimization problems. To be more specific, our proposed algorithms can reduce the computational complexities and improve the utility guarantees for solving nonconvex optimization problems such as training deep neural networks. In the last part of the dissertation, we study the privacy-preserving empirical risk minimization in the distributed setting. In such a setting, we propose a new privacy-preserving framework by combining the multi-party computation (MPC) protocol and differentially private mechanisms and show that our framework can achieve better privacy and utility guarantees compared with existing methods. The methods and techniques proposed in this dissertation form a line of researches that deepens our understandings of the trade-off between privacy, utility and efficient in privacy-preserving machine learning, and could also help us develop more efficient and effective private learning algorithms.
Dissertation Abstracts International
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Author :
language : en
Publisher:
Release Date : 2008
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Dissertations, Academic categories.
Towards A Complete Privacy Preserving Machine Learning Pipeline
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Author : Ali Burak Ünal
language : en
Publisher:
Release Date : 2022
Towards A Complete Privacy Preserving Machine Learning Pipeline written by Ali Burak Ünal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Machine learning has proven its success on various problems from many different domains. Different machine learning algorithms use different approaches to capture the underlying patterns in the data. Even though the amount varies between the machine learning algorithms, they require sufficient amounts of data to recognize those patterns. One of the easiest ways to meet this need of the machine learning algorithms is to use multiple sources generating the same type of data. Such a solution is feasible considering that the speed of data generation and the number of sources generating these data have been increasing in parallel to the developments in technology. One can easily satisfy the desire of the machine learning algorithms for data using these sources. However, this can cause a privacy leakage. The data generated by these sources may contain sensitive information that can be used for undesirable purposes. Therefore, although the machine learning algorithms demand for data, the sources may not be willing or even allowed to share their data. A similar dilemma occurs when the data owner wants to extract useful information from the data by using machine learning algorithms but it does not have enough computational power or knowledge. In this case, the data source may want to outsource this task to external parties that offer machine learning algorithms as a service. Similarly, in this case, the sensitive information in the data can be the decisive factor for the owner not to choose outsourcing, which then ends up with non-utilized data for the owner. In order to address these kinds of dilemmas and issues, this thesis aims to come up with a complete privacy preserving machine learning pipeline. It introduces several studies that address different phases of the pipeline so that all phases of a machine learning algorithm can be performed privately. One of these phases addressed in this thesis is training of a machine learning algorithm. The privacy preserving training of kernel-based machine learning algorithms are addressed in several different works with different cryptographic techniques, one of which is a our newly developed encryption scheme. The different techniques have different advantages over the others. Furthermore, this thesis introduces our study addressing the testing phase of not only the kernel-based machine learning algorithms but also a special type of recurrent neural network, namely recurrent kernel networks, which is the first study performing such an inference, without compromising privacy. To enable the privacy preserving inference on recurrent kernel networks, this thesis introduces a framework, called CECILIA, with two novel functions, which are the exponential and the inverse square root of the Gram matrix, and efficient versions of the existing functions, which are the multiplexer and the most significant bit. Using this framework and other approaches in the corresponding studies, it is possible to perform privacy preserving inference on various pre-trained machine learning algorithms. Besides the training and testing of machine learning algorithms in a privacy preserving way, this thesis also presents a work that aims to evaluate the performance of machine learning algorithms without sacrificing privacy. This work employs CECILIA to realize the area under curve calculation for two different curve-based evaluations, namely the receiver operating characteristic curve and the precision-recall curve, in a privacy preserving manner. All the proposed approaches are shown to be correct using several machine learning tasks and evaluated for the scalability of the parameters of the corresponding system/algorithm using synthetic data. The results show that the privacy preserving training and testing of kernel-based machine learning algorithms is possible with different settings and the privacy preserving inference on a pre-trained recurrent kernel network is feasible using CECILIA. Additionally, CECILIA also allows the exact area under curve computation to evaluate the performance of a machine learning algorithm without compromising privacy.