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Author: Samson Esayas Publisher: ISBN: Category : Languages : en Pages : 39
Book Description
With the growing importance of data for commercial purposes, privacy has attracted considerable attention in competition law discussions, particularly where companies in datarich industries seek a merger or acquisition. Prime examples of such mergers include the Google/DoubleClick, Facebook/WhatsApp and the recent acquisition of LinkedIn by Microsoft. Such mergers are primarily driven by the desire to acquire and combine new data assets that are viewed as a key source of competitive advantage in developing and providing digital services. This development in turn raises novel regulatory questions in competition law including whether and how privacy considerations could be incorporated into merger assessments.One emerging approach, shared both by the European Commission (EC) and the US Federal Trade Commission (FTC), is to factor in privacy-as-a-quality (non-price) competition parameter. This approach treats privacy as a quality component of the product or service offered to consumers and privacy harms as reductions in the quality of the product or service that need to be accounted for in competition analysis. Despite some emerging consensus on how to incorporate privacy, there is much uncertainty and scepticism on what constitutes reduction in privacy, the incentive to reduce privacy and the ultimate anti-competitive effect of such a reduction. This paper identifies and reflects on some of these uncertainties and scepticisms surrounding privacy-as-a-quality parameter including the lack of a link between privacy harms and accumulation of too much information; the lack of economic incentive to reduce privacy; and the alleged trade-off between privacy harms and other quality improvements. Finally, the paper examines the role that data privacy law can play in understanding degradation of privacy in competition law.
Author: Samson Esayas Publisher: ISBN: Category : Languages : en Pages : 39
Book Description
With the growing importance of data for commercial purposes, privacy has attracted considerable attention in competition law discussions, particularly where companies in datarich industries seek a merger or acquisition. Prime examples of such mergers include the Google/DoubleClick, Facebook/WhatsApp and the recent acquisition of LinkedIn by Microsoft. Such mergers are primarily driven by the desire to acquire and combine new data assets that are viewed as a key source of competitive advantage in developing and providing digital services. This development in turn raises novel regulatory questions in competition law including whether and how privacy considerations could be incorporated into merger assessments.One emerging approach, shared both by the European Commission (EC) and the US Federal Trade Commission (FTC), is to factor in privacy-as-a-quality (non-price) competition parameter. This approach treats privacy as a quality component of the product or service offered to consumers and privacy harms as reductions in the quality of the product or service that need to be accounted for in competition analysis. Despite some emerging consensus on how to incorporate privacy, there is much uncertainty and scepticism on what constitutes reduction in privacy, the incentive to reduce privacy and the ultimate anti-competitive effect of such a reduction. This paper identifies and reflects on some of these uncertainties and scepticisms surrounding privacy-as-a-quality parameter including the lack of a link between privacy harms and accumulation of too much information; the lack of economic incentive to reduce privacy; and the alleged trade-off between privacy harms and other quality improvements. Finally, the paper examines the role that data privacy law can play in understanding degradation of privacy in competition law.
Author: Samson Esayas Publisher: ISBN: Category : Languages : en Pages : 62
Book Description
It is widely accepted that firms compete by offering consumers lower prices, high-quality products, and a wide range of choices. With the increasing commercialization of personal, there is now a growing consensus that the level of privacy protection and deployment of Privacy Enhancing Technologies (PETs) could be subject to competition by companies. A case in point is the recognition by the European Commission that data privacy constitutes a key parameter of non-price competition in the market for consumer communications and for professional social networks. This approach treats privacy as a quality, choice or innovation component of the product/service offered to consumers and certain privacy harms as reductions in these parameters that need to be accounted for in the competition analysis. However, little attention has been paid in laying out a concrete theory of harm that outlines how data privacy can be incorporated into competition analysis as a non-price parameter and what constitutes reduction in privacy. This paper is an attempt to fill in this apparent gap. To this end, the paper provides a critical analysis, in light of EU competition law, of three theories harm for incorporating privacy as a non-price competition parameter into merger assessment, namely the privacy-as-a-quality, the consumer choice theory and the maverick-firm theory. Additionally, the paper examines what dimensions of privacy are relevant for competition and what is the (added) value of incorporating privacy into competition analysis.
Author: Cynthia Dwork Publisher: ISBN: 9781601988188 Category : Computers Languages : en Pages : 286
Book Description
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.
Author: Josep Domingo-Ferrer Publisher: Springer ISBN: 3319112570 Category : Computers Languages : en Pages : 376
Book Description
This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2014, held in Ibiza, Spain in September 2014 under the sponsorship of the UNESCO chair in Data Privacy. The 27 revised full papers presented were carefully reviewed and selected from 41 submissions. The scope of the conference is on following topics: tabular data protection, microdata masking, protection using privacy models, synthetic data, record linkage, remote access, privacy-preserving protocols, and case studies.
Author: Erika McCallister Publisher: DIANE Publishing ISBN: 1437934889 Category : Computers Languages : en Pages : 59
Book Description
The escalation of security breaches involving personally identifiable information (PII) has contributed to the loss of millions of records over the past few years. Breaches involving PII are hazardous to both individuals and org. Individual harms may include identity theft, embarrassment, or blackmail. Organ. harms may include a loss of public trust, legal liability, or remediation costs. To protect the confidentiality of PII, org. should use a risk-based approach. This report provides guidelines for a risk-based approach to protecting the confidentiality of PII. The recommend. here are intended primarily for U.S. Fed. gov¿t. agencies and those who conduct business on behalf of the agencies, but other org. may find portions of the publication useful.
Author: Institute of Medicine Publisher: National Academies Press ISBN: 0309124999 Category : Computers Languages : en Pages : 334
Book Description
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.
Author: Björn Lundqvist Publisher: Edward Elgar Publishing ISBN: 1788971833 Category : Law Languages : en Pages : 400
Book Description
The digital economy is gradually gaining traction through a variety of recent technological developments, including the introduction of the Internet of things, artificial intelligence and markets for data. This innovative book contains contributions from leading competition law scholars who map out and investigate the anti-competitive effects that are developing in the digital economy.
Author: National Academies of Sciences, Engineering, and Medicine Publisher: National Academies Press ISBN: 0309465370 Category : Social Science Languages : en Pages : 195
Book Description
The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.
Author: Vijay Varadharajan Publisher: Springer ISBN: 3540477195 Category : Computers Languages : en Pages : 532
Book Description
This book constitutes the refereed proceedings of the 6th Australasian Conference on Information Security and Privacy, ACISP 2001, held in Sydney, Australia, in July 2001. The 38 revised full papers presented together with three invited contributions were carefully reviewed and selected from a total of 91 submissions. Among the topics addressed are systems security, network security, trust and access cotrol, authentication, cryptography, cryptanalysis, digital signatures, elliptic curve cryptology, and secret sharing and threshold schemes.
Author: Samson Esayas
Author: Samson Esayas
Author: Samson Esayas
Author: Cynthia Dwork
Author: Josep Domingo-Ferrer
Author: Erika McCallister
Author: Institute of Medicine
Author: Björn Lundqvist
Author: National Academies of Sciences, Engineering, and Medicine
Author: Vijay Varadharajan
Author: