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.

Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks. This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations. We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it. The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.

1. Equip professionals with holistic and structured knowledge regarding establishing and implementing privacy framework and program. 2. Gain practical guidance, tools, and templates to manage complex privacy and data protection subjects with cross-functional teams. 3. Gain the knowledge in measuring privacy program and operating it in a more efficient and effective manner.

This volume draws together OECD work to date on measures for ensuring effective privacy protection on global networks while continuing to allow the transborder flow of personal data.

As jurisdictions increasingly pass new cybersecurity and privacy laws, it is crucial that attorneys secure a working knowledge of information technology to effectively advise organizations that collect and process data. This essential book—now extensively updated to reflect the dramatic legal changes that have taken place in the few short years since its first edition—remains the preeminent in-depth survey and analysis of privacy and cybersecurity laws worldwide. It also provides a deeply informed guide on how to apply legal requirements to protect an organization’s interests and anticipate future compliance developments. With detailed attention to relevant supranational, regional, and national privacy and data protection laws and frameworks, the author describes and analyzes the legal strategies and responsibilities attached to the following and more: prompt, secure ways to identify threats, manage vulnerabilities, and respond to “incidents” and data breaches; most common types of cyberattacks used today; transparency and consent; rights of revocation, erasure, and correction; de-identification and anonymization procedures; data localization; cross-jurisdictional data transfer; contract negotiation; encryption, de-identification, anonymization, and pseudonymization; and Artificial Intelligence as an emerging technology that will require more dynamic and challenging conversations. Balancing legal knowledge with technical awareness and business acumen, this book is an indispensable resource for attorneys who must provide advice on strategic implementations of new technologies, advise on the impact of certain laws on the enterprise, interpret complex cybersecurity and privacy contractual language, and participate in incident response and data breach activities. It will also be of value to other practitioners, such as security personnel and compliance professionals, who will benefit from a broad perspective exploring privacy and data protection laws and their connection with security technologies and broader organizational compliance objectives.

In the realm of health care, privacy protections are needed to preserve patients' dignity and prevent possible harms. Ten years ago, to address these concerns as well as set guidelines for ethical health research, Congress called for a set of federal standards now known as the HIPAA Privacy Rule. In its 2009 report, Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research, the Institute of Medicine's Committee on Health Research and the Privacy of Health Information concludes that the HIPAA Privacy Rule does not protect privacy as well as it should, and that it impedes important health research.