A penetrating and insightful study of privacy and security in telecommunications for a post-9/11, post-Patriot Act world. Telecommunication has never been perfectly secure. The Cold War culture of recording devices in telephone receivers and bugged embassy offices has been succeeded by a post-9/11 world of NSA wiretaps and demands for data retention. Although the 1990s battle for individual and commercial freedom to use cryptography was won, growth in the use of cryptography has been slow. Meanwhile, regulations requiring that the computer and communication industries build spying into their systems for government convenience have increased rapidly. The application of the 1994 Communications Assistance for Law Enforcement Act has expanded beyond the intent of Congress to apply to voice over Internet Protocol (VoIP) and other modern data services; attempts are being made to require ISPs to retain their data for years in case the government wants it; and data mining techniques developed for commercial marketing applications are being applied to widespread surveillance of the population. In Privacy on the Line, Whitfield Diffie and Susan Landau strip away the hype surrounding the policy debate over privacy to examine the national security, law enforcement, commercial, and civil liberties issues. They discuss the social function of privacy, how it underlies a democratic society, and what happens when it is lost. This updated and expanded edition revises their original -- and prescient -- discussions of both policy and technology in light of recent controversies over NSA spying and other government threats to communications privacy.

Bulletproof your organization against data breach, identity theft, and corporate espionage In this updated and revised edition of Privacy Means Profit, John Sileo demonstrates how to keep data theft from destroying your bottom line, both personally and professionally. In addition to sharing his gripping tale of losing $300,000 and his business to data breach, John writes about the risks posed by social media, travel theft, workplace identity theft, and how to keep it from happening to you and your business. By interlacing his personal experience with cutting-edge research and unforgettable stories, John not only inspires change inside of your organization, but outlines a simple framework with which to build a Culture of Privacy. This book is a must-read for any individual with a Social Security Number and any business leader who doesn't want the negative publicity, customer flight, legal battles and stock depreciation resulting from data breach. Protect your net worth and bottom line using the 7 Mindsets of a Spy Accumulate Layers of Privacy Eliminate the Source Destroy Data Risk Lock Your Assets Evaluate the Offer Interrogate the Enemy Monitor the Signs In this revised edition, John includes an 8th Mindset, Adaptation, which serves as an additional bridge between personal protection and bulletproofing your organization. Privacy Means Profit offers a one-stop guide to protecting what's most important and most at risk-your essential business and personal data.

This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Privacy Enhancing Technologies, PET 2002, held in San Francisco, CA, USA, in April 2002. The 17 revised full papers presented were carefully selected during two rounds of reviewing and improvement. Among the topics addressed are Internet security, private authentication, information theoretic anonymity, anonymity measuring, enterprise privacy practices, service architectures for privacy, intersection attacks, online trust negotiation, random data perturbation, Website fingerprinting, Web user privacy, TCP timestamps, private information retrieval, and unobservable Web surfing.

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.

Reproduction of the original: The Right to Privacy by Samuel D. Warren, Louis D. Brandeis

This handbook covers Electronic Medical Record (EMR) systems, which enable the storage, management, and sharing of massive amounts of demographic, diagnosis, medication, and genomic information. It presents privacy-preserving methods for medical data, ranging from laboratory test results to doctors’ comments. The reuse of EMR data can greatly benefit medical science and practice, but must be performed in a privacy-preserving way according to data sharing policies and regulations. Written by world-renowned leaders in this field, each chapter offers a survey of a research direction or a solution to problems in established and emerging research areas. The authors explore scenarios and techniques for facilitating the anonymization of different types of medical data, as well as various data mining tasks. Other chapters present methods for emerging data privacy applications and medical text de-identification, including detailed surveys of deployed systems. A part of the book is devoted to legislative and policy issues, reporting on the US and EU privacy legislation and the cost of privacy breaches in the healthcare domain. This reference is intended for professionals, researchers and advanced-level students interested in safeguarding medical data.