Privacy, Data Harvesting, and You

Privacy, Data Harvesting, and You PDF Author: Jeri Freedman
Publisher: The Rosen Publishing Group, Inc
ISBN: 1508188327
Category : Young Adult Nonfiction
Languages : en
Pages : 64

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Book Description
One of the most widespread online practices today is data harvesting, the collection of users, personal data and information about their activities. Data harvesting raises significant issues about the right to privacy. This informative narrative explains what data harvesting and data mining are and how they are carried out. The importance of privacy is covered, as well as two of the most common applications of data harvesting and data mining: the selling of products and services, and the influencing of people's attitudes toward political issues. Teens learn ways that they can safeguard their data to protect their privacy.

Privacy, Data Harvesting, and You

Privacy, Data Harvesting, and You PDF Author: Jeri Freedman
Publisher: The Rosen Publishing Group, Inc
ISBN: 1508188327
Category : Young Adult Nonfiction
Languages : en
Pages : 64

Get Book Here

Book Description
One of the most widespread online practices today is data harvesting, the collection of users, personal data and information about their activities. Data harvesting raises significant issues about the right to privacy. This informative narrative explains what data harvesting and data mining are and how they are carried out. The importance of privacy is covered, as well as two of the most common applications of data harvesting and data mining: the selling of products and services, and the influencing of people's attitudes toward political issues. Teens learn ways that they can safeguard their data to protect their privacy.

Privacy and Big Data

Privacy and Big Data PDF Author: Terence Craig
Publisher: "O'Reilly Media, Inc."
ISBN: 1449316700
Category : Computers
Languages : en
Pages : 95

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Book Description
Much of what constitutes Big Data is information about us. Through our online activities, we leave an easy-to-follow trail of digital footprints that reveal who we are, what we buy, where we go, and much more. This eye-opening book explores the raging privacy debate over the use of personal data, with one undeniable conclusion: once data's been collected, we have absolutely no control over who uses it or how it is used. Personal data is the hottest commodity on the market today—truly more valuable than gold. We are the asset that every company, industry, non-profit, and government wants. Privacy and Big Data introduces you to the players in the personal data game, and explains the stark differences in how the U.S., Europe, and the rest of the world approach the privacy issue. You'll learn about: Collectors: social networking titans that collect, share, and sell user data Users: marketing organizations, government agencies, and many others Data markets: companies that aggregate and sell datasets to anyone Regulators: governments with one policy for commercial data use, and another for providing security

Privacy is Power

Privacy is Power PDF Author: Carissa Veliz
Publisher: Melville House
ISBN: 1612199151
Category : Social Science
Languages : en
Pages : 304

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Book Description
An Economist Book of the Year Every minute of every day, our data is harvested and exploited… It is time to pull the plug on the surveillance economy. Governments and hundreds of corporations are spying on you, and everyone you know. They're not just selling your data. They're selling the power to influence you and decide for you. Even when you've explicitly asked them not to. Reclaiming privacy is the only way we can regain control of our lives and our societies. These governments and corporations have too much power, and their power stems from us--from our data. Privacy is as collective as it is personal, and it's time to take back control. Privacy Is Power tells you how to do exactly that. It calls for the end of the data economy and proposes concrete measures to bring that end about, offering practical solutions, both for policymakers and ordinary citizens.

Data Privacy

Data Privacy PDF Author: Nishant Bhajaria
Publisher: Simon and Schuster
ISBN: 1617298999
Category : Computers
Languages : en
Pages : 382

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Book Description
Privacy engineering : why it's needed, how to scale it -- Understanding data and privacy -- Data classification -- Data inventory -- Data sharing -- The technical privacy review -- Data deletion -- Exporting user data : data subject access requests -- Building a consent management platform -- Closing security vulnerabilities -- Scaling, hiring, and considering regulations.

Cyber Privacy

Cyber Privacy PDF Author: April Falcon Doss
Publisher: BenBella Books
ISBN: 1950665534
Category : History
Languages : en
Pages : 335

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Book Description
"Chilling, eye-opening, and timely, Cyber Privacy makes a strong case for the urgent need to reform the laws and policies that protect our personal data. If your reaction to that statement is to shrug your shoulders, think again. As April Falcon Doss expertly explains, data tracking is a real problem that affects every single one of us on a daily basis." —General Michael V. Hayden, USAF, Ret., former Director of CIA and NSA and former Principal Deputy Director of National Intelligence You're being tracked. Amazon, Google, Facebook, governments. No matter who we are or where we go, someone is collecting our data: to profile us, target us, assess us; to predict our behavior and analyze our attitudes; to influence the things we do and buy—even to impact our vote. If this makes you uneasy, it should. We live in an era of unprecedented data aggregation, and it's never been more difficult to navigate the trade-offs between individual privacy, personal convenience, national security, and corporate profits. Technology is evolving quickly, while laws and policies are changing slowly. You shouldn't have to be a privacy expert to understand what happens to your data. April Falcon Doss, a privacy expert and former NSA and Senate lawyer, has seen this imbalance in action. She wants to empower individuals and see policy catch up. In Cyber Privacy, Doss demystifies the digital footprints we leave in our daily lives and reveals how our data is being used—sometimes against us—by the private sector, the government, and even our employers and schools. She explains the trends in data science, technology, and the law that impact our everyday privacy. She tackles big questions: how data aggregation undermines personal autonomy, how to measure what privacy is worth, and how society can benefit from big data while managing its risks and being clear-eyed about its cost. It's high time to rethink notions of privacy and what, if anything, limits the power of those who are constantly watching, listening, and learning about us. This book is for readers who want answers to three questions: Who has your data? Why should you care? And most important, what can you do about it?

Data Matters

Data Matters PDF Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 030948247X
Category : Science
Languages : en
Pages : 103

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Book Description
In an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, to specific project-generated dataâ€"this is just a narrow set of examples of research data but illustrates the breadth of possibilities. The uses of these data are various and require accounting for the effects of data access, use, and sharing on many different parties. Cultural, legal, policy, and technical concerns are also important determinants of what can be done in the realms of maintaining privacy, confidentiality, and security, and ethics is a lens through which the issues of data, data sharing, and research agreements can be viewed as well. A workshop held on March 14-16, 2018, in Washington, DC explored the changing opportunities and risks of data management and use across disciplinary domains. The third workshop in a series, participants gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domainsâ€"all within the context of international research agreements. This publication summarizes the presentations and discussions from the workshop.

Privacy, Big Data, and the Public Good

Privacy, Big Data, and the Public Good PDF Author: Julia Lane
Publisher: Cambridge University Press
ISBN: 1107067359
Category : Computers
Languages : en
Pages : 343

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Book Description
Data access is essential for serving the public good. This book provides new frameworks to address the resultant privacy issues.

Data Privacy

Data Privacy PDF Author: Nataraj Venkataramanan
Publisher: CRC Press
ISBN: 1315353768
Category : Computers
Languages : en
Pages : 206

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Book Description
The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.

Everybody Lies

Everybody Lies PDF Author: Seth Stephens-Davidowitz
Publisher: HarperCollins
ISBN: 0062390872
Category : Social Science
Languages : en
Pages : 325

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Book Description
Foreword by Steven Pinker Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions. By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable. Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women? Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.

Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning PDF Author: J. Morris Chang
Publisher: Simon and Schuster
ISBN: 1638352755
Category : Computers
Languages : en
Pages : 334

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Book Description
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)