Protecting Your Privacy In A Data Driven World

At what point does the sacrifice to our personal information outweigh the public good? If public policymakers had access to our personal and confidential data, they could make more evidence-based, data-informed decisions that could accelerate economic recovery and improve COVID-19 vaccine distribution. However, access to personal data comes at a steep privacy cost for contributors, especially underrepresented groups. Protecting Your Privacy in a Data-Driven World is a practical, nontechnical guide that explains the importance of balancing these competing needs and calls for careful consideration of how data are collected and disseminated by our government and the private sector. Not addressing these concerns can harm the same communities policymakers are trying to protect through data privacy and confidentiality legislation.

At what point does the sacrifice to our personal information outweigh the public good? If public policymakers had access to our personal and confidential data, they could make more evidence-based, data-informed decisions that could accelerate economic recovery and improve COVID-19 vaccine distribution. However, access to personal data comes at a steep privacy cost for contributors, especially underrepresented groups. Protecting Your Privacy in a Data-Driven World is a practical, nontechnical guide that explains the importance of balancing these competing needs and calls for careful consideration of how data are collected and disseminated by our government and the private sector. Not addressing these concerns can harm the same communities policymakers are trying to protect through data privacy and confidentiality legislation.

Digital devices have made our busy lives a little easier and they do great things for us, too – we get just-in-time coupons, directions, and connection with loved ones while stuck on an airplane runway. Yet, these devices, though we love them, can invade our privacy in ways we are not even aware of. The digital devices send and collect data about us whenever we use them, but that data is not always safeguarded the way we assume it should be to protect our privacy. Privacy is complex and personal. Many of us do not know the full extent to which data is collected, stored, aggregated, and used. As recent revelations indicate, we are subject to a level of data collection and surveillance never before imaginable. While some of these methods may, in fact, protect us and provide us with information and services we deem to be helpful and desired, others can turn out to be insidious and over-arching. Privacy in the Age of Big Data highlights the many positive outcomes of digital surveillance and data collection while also outlining those forms of data collection to which we do not always consent, and of which we are likely unaware, as well as the dangers inherent in such surveillance and tracking. Payton and Claypoole skillfully introduce readers to the many ways we are “watched” and how to change behaviors and activities to recapture and regain more of our privacy. The authors suggest remedies from tools, to behavior changes, to speaking out to politicians to request their privacy back. Anyone who uses digital devices for any reason will want to read this book for its clear and no-nonsense approach to the world of big data and what it means for all of us.

Engineer privacy into your systems with these hands-on techniques for data governance, legal compliance, and surviving security audits. In Data Privacy you will learn how to: Classify data based on privacy risk Build technical tools to catalog and discover data in your systems Share data with technical privacy controls to measure reidentification risk Implement technical privacy architectures to delete data Set up technical capabilities for data export to meet legal requirements like Data Subject Asset Requests (DSAR) Establish a technical privacy review process to help accelerate the legal Privacy Impact Assessment (PIA) Design a Consent Management Platform (CMP) to capture user consent Implement security tooling to help optimize privacy Build a holistic program that will get support and funding from the C-Level and board Data Privacy teaches you to design, develop, and measure the effectiveness of privacy programs. You’ll learn from author Nishant Bhajaria, an industry-renowned expert who has overseen privacy at Google, Netflix, and Uber. The terminology and legal requirements of privacy are all explained in clear, jargon-free language. The book’s constant awareness of business requirements will help you balance trade-offs, and ensure your user’s privacy can be improved without spiraling time and resource costs. About the technology Data privacy is essential for any business. Data breaches, vague policies, and poor communication all erode a user’s trust in your applications. You may also face substantial legal consequences for failing to protect user data. Fortunately, there are clear practices and guidelines to keep your data secure and your users happy. About the book Data Privacy: A runbook for engineers teaches you how to navigate the trade-off s between strict data security and real world business needs. In this practical book, you’ll learn how to design and implement privacy programs that are easy to scale and automate. There’s no bureaucratic process—just workable solutions and smart repurposing of existing security tools to help set and achieve your privacy goals. What's inside Classify data based on privacy risk Set up capabilities for data export that meet legal requirements Establish a review process to accelerate privacy impact assessment Design a consent management platform to capture user consent About the reader For engineers and business leaders looking to deliver better privacy. About the author Nishant Bhajaria leads the Technical Privacy and Strategy teams for Uber. His previous roles include head of privacy engineering at Netflix, and data security and privacy at Google. Table of Contents PART 1 PRIVACY, DATA, AND YOUR BUSINESS 1 Privacy engineering: Why it’s needed, how to scale it 2 Understanding data and privacy PART 2 A PROACTIVE PRIVACY PROGRAM: DATA GOVERNANCE 3 Data classification 4 Data inventory 5 Data sharing PART 3 BUILDING TOOLS AND PROCESSES 6 The technical privacy review 7 Data deletion 8 Exporting user data: Data Subject Access Requests PART 4 SECURITY, SCALING, AND STAFFING 9 Building a consent management platform 10 Closing security vulnerabilities 11 Scaling, hiring, and considering regulations

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.

Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout.

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.

Buying the safest car for your family shouldn’t be up for debate. Yet for decades, car safety advocates, manufacturers, and lawmakers in the United States have clashed over whether to make automobiles safer. All sides armed themselves with data in the hopes of winning the great car safety debates. In this way, crash statistics and the analysts who studied them made history. But data were always in the backseat, merely supporting different points of view. That is, until now. With car safety, it’s the value we place on every human life that counts. Automobile safety expert Dr. Norma Faris Hubele delivers a lively discussion of the role data play in protecting you and your family on the road. You’ll gain a greater appreciation for how: A World War I pilot’s near-death experience birthed the U.S. car safety movement Data from real car crashes helped create the first vehicle safety standards A shift toward fuel-efficient cars affected fatality risk in the 1970s–1980s versus now Vehicle size has changed, and the problems that creates for you and others sharing the road Car safety rating systems, even when limited, empower consumers and motivate manufacturers Federal regulators decide whether to issue a safety recall on your vehicle Data’s role is evolving with the advent of driver-assist and self-driving technologies Further information can be found on the book's website: www.TheAutoProfessor.com/book [US only].

Demonstrates how modern statistical techniques can measure the impact of counterfactual decisions. Examines the importance of counterfactual reasoning for both modern scholars and historical actors. Combines historical narrative, mathematical precision and data to create a straightforward presentation of both factual and counterfactual military history. Provides an original contribution to the debate over the validity and rigour of works of counterfactual history Written in a manner accessible to readers who have no formal training in History or Statistics.

How do you learn about what’s going on in the world? Did a news headline grab your attention? Did a news story report on recent research? What do you need to know to be a critical consumer of the news you read? If you are looking to start developing your data self-defense and critical news consumption skills, this book is for you! It reflects a long-term collaboration between a statistician and a journalist to shed light on the statistics behind the stories and the stories behind the statistics. The only prerequisite for enjoying this book is an interest in developing the skills and insights for better understanding news stories that incorporate quantitative information. Chapters in Statistics Behind the Headlines kick off with a news story headline and a summary of the story itself. The meat of each chapter consists of an exploration of the statistical and journalism concepts needed to understand the data analyzed and reported in the story. The chapters are organized around these sections: What ideas will you encounter in this chapter? What is claimed? Is it appropriate? Who is claiming this? Why is it claimed? What makes this a story worth telling? Is this a good measure of impact? How is the claim supported? What evidence is reported? What is the quality/strength of the evidence? Does the claim seem reasonable? How does this claim fit with what is already known? How much does this matter? Considering the coverage Chapters close with connections to the Stats + Stories podcast.