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Privacy Risk Analysis fills a gap in the existing literature by providing an introduction to the basic notions, requirements, and main steps of conducting a privacy risk analysis. The deployment of new information technologies can lead to significant privacy risks and a privacy impact assessment should be conducted before designing a product or system that processes personal data. However, if existing privacy impact assessment frameworks and guidelines provide a good deal of details on organizational aspects (including budget allocation, resource allocation, stakeholder consultation, etc.), they are much vaguer on the technical part, in particular on the actual risk assessment task. For privacy impact assessments to keep up their promises and really play a decisive role in enhancing privacy protection, they should be more precise with regard to these technical aspects. This book is an excellent resource for anyone developing and/or currently running a risk analysis as it defines the notions of personal data, stakeholders, risk sources, feared events, and privacy harms all while showing how these notions are used in the risk analysis process. It includes a running smart grids example to illustrate all the notions discussed in the book.

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The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guarantees they offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer.

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