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Reproduction of the original: The Right to Privacy by Samuel D. Warren, Louis D. Brandeis
Throughout the history of business employees had to adapt to managers and managers had to adapt to organizations. In the future this is reversed with managers and organizations adapting to employees. This means that in order to succeed and thrive organizations must rethink and challenge everything they know about work. The demographics of employees are changing and so are employee expectations, values, attitudes, and styles of working. Conventional management models must be replaced with leadership approaches adapted to the future employee. Organizations must also rethink their traditional structure, how they empower employees, and what they need to do to remain competitive in a rapidly changing world. This is a book about how employees of the future will work, how managers will lead, and what organizations of the future will look like. The Future of Work will help you: Stay ahead of the competition Create better leaders Tap into the freelancer economy Attract and retain top talent Rethink management Structure effective teams Embrace flexible work environments Adapt to the changing workforce Build the organization of the future And more The book features uncommon examples and easy to understand concepts which will challenge and inspire you to work differently.
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)
An argument for retaining the notion of personal property in the products we “buy” in the digital marketplace. If you buy a book at the bookstore, you own it. You can take it home, scribble in the margins, put in on the shelf, lend it to a friend, sell it at a garage sale. But is the same thing true for the ebooks or other digital goods you buy? Retailers and copyright holders argue that you don't own those purchases, you merely license them. That means your ebook vendor can delete the book from your device without warning or explanation—as Amazon deleted Orwell's 1984 from the Kindles of surprised readers several years ago. These readers thought they owned their copies of 1984. Until, it turned out, they didn't. In The End of Ownership, Aaron Perzanowski and Jason Schultz explore how notions of ownership have shifted in the digital marketplace, and make an argument for the benefits of personal property. Of course, ebooks, cloud storage, streaming, and other digital goods offer users convenience and flexibility. But, Perzanowski and Schultz warn, consumers should be aware of the tradeoffs involving user constraints, permanence, and privacy. The rights of private property are clear, but few people manage to read their end user agreements. Perzanowski and Schultz argue that introducing aspects of private property and ownership into the digital marketplace would offer both legal and economic benefits. But, most important, it would affirm our sense of self-direction and autonomy. If we own our purchases, we are free to make whatever lawful use of them we please. Technology need not constrain our freedom; it can also empower us.
As a result, individuals and organized groups are fighting to hold onto independence and freedom against those trying to expose the private sector to public scrutiny.
Can open source software—software that is usually available without charge and that individuals are free to modify—survive against the fierce competition of proprietary software, such as Microsoft Windows? Should the government intervene on its behalf? This book addresses a host of issues raised by the rapid growth of open source software, including government subsidies for research and development, government procurement policy, and patent and copyright policy. Contributors offer diverse perspectives on a phenomenon that has become a lightning rod for controversy in the field of information technology. Contributors include James Bessen (Research on Innovation), David S. Evans (National Economic Research Associates), Lawrence Lessig (Stanford University), Bradford L. Smith (Microsoft Corporation), and Robert W. Hahn (director, AEI-Brookings Joint Center).
Thoroughly updates the first edition by addressing the significant advances in data-driven technologies, their intrusion deeper in our lives, the limits on data collection newly required by governments in North America and Europe, and the new security challenges of a world rife with ransomware and hacking.
"The United States Code is the official codification of the general and permanent laws of the United States of America. The Code was first published in 1926, and a new edition of the code has been published every six years since 1934. The 2012 edition of the Code incorporates laws enacted through the One Hundred Twelfth Congress, Second Session, the last of which was signed by the President on January 15, 2013. It does not include laws of the One Hundred Thirteenth Congress, First Session, enacted between January 2, 2013, the date it convened, and January 15, 2013. By statutory authority this edition may be cited "U.S.C. 2012 ed." As adopted in 1926, the Code established prima facie the general and permanent laws of the United States. The underlying statutes reprinted in the Code remained in effect and controlled over the Code in case of any discrepancy. In 1947, Congress began enacting individual titles of the Code into positive law. When a title is enacted into positive law, the underlying statutes are repealed and the title then becomes legal evidence of the law. Currently, 26 of the 51 titles in the Code have been so enacted. These are identified in the table of titles near the beginning of each volume. The Law Revision Counsel of the House of Representatives continues to prepare legislation pursuant to 2 U.S.C. 285b to enact the remainder of the Code, on a title-by-title basis, into positive law. The 2012 edition of the Code was prepared and published under the supervision of Ralph V. Seep, Law Revision Counsel. Grateful acknowledgment is made of the contributions by all who helped in this work, particularly the staffs of the Office of the Law Revision Counsel and the Government Printing Office"--Preface.