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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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)

The human quest for self-understanding is ancient. It transcends the boundaries between ordinary folk and philosophers and it over- laps with many academic disciplines, including psychology, sociology, philosophy and theology. Actually, the quest is not essentially academic; it is a human quest, pursued by persons in every age. With this in mind, philosopher C. Stephen Evans takes a look at the human sciences and their contribution to this self-understanding. Evans first presents a basic problem in these sciences today: the attack on the concept of personhood. He reviews the contemporary understanding of mind and brain: Is a person only a thinking machine or a programmed organism? Then he evaluates the impact of Auguste Comte, Sigmund Freud, J.B. Watson, B.F. Skinner and Emile Durkheim on what Evans terms ?

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.

Both epic and intimate, this is the story of the fight to save the world’s architectural and cultural heritage as it is embodied in the extraordinary buildings and urban spaces of the great cities of Asia, the Americas, and Europe. Never before have the complexities and dramas of urban preservation been as keenly documented as inPreserving the World’s Great Cities. In researching this important work, Anthony Tung traveled throughout the world to visit remarkable buildings and districts in China, Italy, Greece, the U.S., Japan, and elsewhere. Everywhere he found both the devastating legacy of war, economics, and indifference and the accomplishments of people who have worked and sometimes risked their lives to preserve and renew the most meaningful urban expressions of the human spirit. From Singapore’s blind rush to become the most modern city of the East to Warsaw’s poignant and heroic effort to resurrect itself from the Nazis’ systematic campaign of physical and cultural obliteration, from New York and Rome to Kyoto and Cairo, we see the city as an expression of the best and worst within us. This is essential reading for fans of Jane Jacobs and Witold Rybczynski and everyone who is concerned about urban preservation.

"His book provides a comprehensive survey of G. W. Leibniz's deep and complex engagement with the sciences of life, in areas as diverse as medicine, physiology, taxonomy, generation theory, and paleontology. It is shown that these sundry interests were not only relevant to his core philosophical interests, but indeed often provided the insights that in part led to some of his most familiar philosophical doctrines, including the theory of corporeal substance and the theory of organic preformation"--Provided by publisher.

"A selection of prose writing from American poet Philip Lamantia (1927-2005), edited by poet Garrett Caples"--