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Mobile app analytics gathers detailed data about millions of app users. Both customers and governments are becoming increasingly concerned about the privacy implications of such data gathering. Thus, it is highly desirable to design privacy-preserving versions of mobile app analytics. We aim to achieve this goal using differential privacy, a leading algorithm design framework for privacy-preserving data analysis. We apply differential privacy to dynamically-created content that is retrieved from a content server and is displayed to the app user. User interactions with this content are then reported to the app analytics infrastructure. Unlike problems considered in related prior work, such analytics could potentially convey a wealth of sensitive information---for example, about an app user's political beliefs, dietary choices, health conditions, or travel interests. To provide rigorous privacy protections for this information, we design a differentially-private solution for such data gathering. Our first contribution is a differentially-private scheme for mobile app analytics of such content. We first present a conceptual design for this data collection.
This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.
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.
This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.
Nearly two decades after the EU first enacted data protection rules, key questions about the nature and scope of this EU policy, and the harms it seeks to prevent, remain unanswered. The inclusion of a Right to Data Protection in the EU Charter has increased the salience of these questions, which must be addressed in order to ensure the legitimacy, effectiveness and development of this Charter right and the EU data protection regime more generally. The Foundations of EU Data Protection Law is a timely and important work which sheds new light on this neglected area of law, challenging the widespread assumption that data protection is merely a subset of the right to privacy. By positioning EU data protection law within a comprehensive conceptual framework, it argues that data protection has evolved from a regulatory instrument into a fundamental right in the EU legal order and that this right grants individuals more control over more forms of data than the right to privacy. It suggests that this dimension of the right to data protection should be explicitly recognised, while identifying the practical and conceptual limits of individual control over personal data. At a time when EU data protection law is sitting firmly in the international spotlight, this book offers academics, policy-makers, and practitioners a coherent vision for the future of this key policy and fundamental right in the EU legal order, and how best to realise it.
This book gathers and analyzes the latest attacks, solutions, and trends in mobile networks. Its broad scope covers attacks and solutions related to mobile networks, mobile phone security, and wireless security. It examines the previous and emerging attacks and solutions in the mobile networking worlds, as well as other pertinent security issues. The many attack samples present the severity of this problem, while the delivered methodologies and countermeasures show how to build a truly secure mobile computing environment.
This book examines issues and implications of digital and social media marketing for emerging markets. These markets necessitate substantial adaptations of developed theories and approaches employed in the Western world. The book investigates problems specific to emerging markets, while identifying new theoretical constructs and practical applications of digital marketing. It addresses topics such as electronic word of mouth (eWOM), demographic differences in digital marketing, mobile marketing, search engine advertising, among others. A radical increase in both temporal and geographical reach is empowering consumers to exert influence on brands, products, and services. Information and Communication Technologies (ICTs) and digital media are having a significant impact on the way people communicate and fulfil their socio-economic, emotional and material needs. These technologies are also being harnessed by businesses for various purposes including distribution and selling of goods, retailing of consumer services, customer relationship management, and influencing consumer behaviour by employing digital marketing practices. This book considers this, as it examines the practice and research related to digital and social media marketing.
This book constitutes the refereed conference proceedings of the 4th Annual Privacy Forum, APF 2016, held in Frankfurt/Main, Germany, in September 2016. The 12 revised full papers presented in this volume were carefully reviewed and selected from 32 submissions. The papers are organized in three sessions: eIDAS and data protection regulation; IoT and public clouds; and privacy policies and privacy risk presentation.
What happens when media technologies are able to interpret our feelings, emotions, moods, and intentions? In this cutting edge new book, Andrew McStay explores that very question and argues that these abilities result in a form of technological empathy. Offering a balanced and incisive overview of the issues raised by ‘Emotional AI’, this book: Provides a clear account of the social benefits and drawbacks of new media trends and technologies such as emoji, wearables and chatbots Demonstrates through empirical research how ‘empathic media’ have been developed and introduced both by start-ups and global tech corporations such as Facebook Helps readers understand the potential implications on everyday life and social relations through examples such as video-gaming, facial coding, virtual reality and cities Calls for a more critical approach to the rollout of emotional AI in public and private spheres Combining established theory with original analysis, this book will change the way students view, use and interact with new technologies. It should be required reading for students and researchers in media, communications, the social sciences and beyond.