This book provides a database researcher or designer a complete, yet concise, overview of differential privacy and its deployment in database systems.

The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.

This book constitutes the refereed proceedings of the 14th International Conference on Information Security, ISC 2011, held in Xi'an, China, in October 2011. The 25 revised full papers were carefully reviewed and selected from 95 submissions. The papers are organized in topical sections on attacks; protocols; public-key cryptosystems; network security; software security; system security; database security; privacy; digital signatures.

This book constitutes the proceedings of the Second International Conference on HCI for Cybersecurity, Privacy and Trust, HCI-CPT 2020, held as part of the 22nd International Conference, HCI International 2020, which took place in Copenhagen, Denmark, in July 2020. The total of 1439 papers and 238 posters included in the 37 HCII 2020 proceedings volumes was carefully reviewed and selected from 6326 submissions. HCI-CPT 2020 includes a total of 45 regular papers; they were organized in topical sections named: human factors in cybersecurity; privacy and trust; usable security approaches. As a result of the Danish Government's announcement, dated April21, 2020, to ban all large events (above 500 participants) until September 1, 2020, the HCII 2020 conference was held virtually.

This book presents a comprehensive mathematical theory that explains precisely what information flow is, how it can be assessed quantitatively – so bringing precise meaning to the intuition that certain information leaks are small enough to be tolerated – and how systems can be constructed that achieve rigorous, quantitative information-flow guarantees in those terms. It addresses the fundamental challenge that functional and practical requirements frequently conflict with the goal of preserving confidentiality, making perfect security unattainable. Topics include: a systematic presentation of how unwanted information flow, i.e., "leaks", can be quantified in operationally significant ways and then bounded, both with respect to estimated benefit for an attacking adversary and by comparisons between alternative implementations; a detailed study of capacity, refinement, and Dalenius leakage, supporting robust leakage assessments; a unification of information-theoretic channels and information-leaking sequential programs within the same framework; and a collection of case studies, showing how the theory can be applied to interesting realistic scenarios. The text is unified, self-contained and comprehensive, accessible to students and researchers with some knowledge of discrete probability and undergraduate mathematics, and contains exercises to facilitate its use as a course textbook.

This book constitutes the refereed proceedings of the 29th Annual International Cryptology Conference, CRYPTO 2009, held in Santa Barbara, CA, USA in August 2009. The 38 revised full papers presented were carefully reviewed and selected from 213 submissions. Addressing all current foundational, theoretical and research aspects of cryptology, cryptography, and cryptanalysis as well as advanced applications, the papers are organized in topical sections on key leakage, hash-function cryptanalysis, privacy and anonymity, interactive proofs and zero-knowledge, block-cipher cryptanalysis, modes of operation, elliptic curves, cryptographic hardness, merkle puzzles, cryptography in the physical world, attacks on signature schemes, secret sharing and secure computation, cryptography and game-theory, cryptography and lattices, identity-based encryption and cryptographers’ toolbox.

This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2018, held in Valencia, Spain, in September 2018 under the sponsorship of the UNESCO Chair in Data Privacy. The 23 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers are organized into the following topics: tabular data protection; synthetic data; microdata and big data masking; record linkage; and spatial and mobility data. Chapter "SwapMob: Swapping Trajectories for Mobility Anonymization" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.