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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. 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This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy.\u003c\/p\u003e\u003cp\u003eThere is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eLarge-Scale Data Analytics\u003c\/i\u003e is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. 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It is primarily addressed to computer science and statistics researchers and educators, who are interested in topics related to mobility privacy. This handbook will also be valuable to industry developers, as it explains the state-of-the-art algorithms for offering privacy. By discussing a wide range of privacy techniques, providing in-depth coverage of the most important ones, and highlighting promising avenues for future research, this handbook also aims at attracting computer science and statistics students to this interesting field of research.\u003c\/p\u003e\n\n\u003cp\u003eThe advances in mobile devices and positioning technologies, together with the progress in spatiotemporal database research, have made possible the tracking of mobile devices (and their human companions) at very high accuracy, while supporting the efficient storage ofmobility data in data warehouses, which this handbook illustrates. This has provided the means to collect, store and process mobility data of an unprecedented quantity, quality and timeliness. As ubiquitous computing pervades our society, user mobility data represents a very useful but also extremely sensitive source of information. On one hand, the movement traces that are left behind by the mobile devices of the users can be very useful in a wide spectrum of applications such as urban planning, traffic engineering, and environmental pollution management. On the other hand, the disclosure of mobility data to third parties may severely jeopardize the privacy of the users whose movement is recorded, leading to abuse scenarios such as user tailing and profiling.\u003c\/p\u003e\n\n\u003cp\u003e A significant amount of research work has been conducted in the last 15 years in the area of mobility data privacy and important research directions, such as privacy-preserving mobility data management, privacy inlocation sensing technologies and location-based services, privacy in vehicular communication networks, privacy in location-based social networks, privacy in participatory sensing systems which this handbook addresses.. This handbook  also identifies important privacy gaps in the use of mobility data and has resulted to the adoption of international laws for location privacy protection (e.g., in EU, US, Canada, Australia, New Zealand, Japan, Singapore), as well as to a large number of interesting technologies for privacy-protecting mobility data, some of which have been made available through open-source systems and featured in real-world applications.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52595322487057,"sku":"NLS9783030074593","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783030074593.jpg?v=1761070892"},{"product_id":"handbook-of-mobile-data-privacy-book-aris-gkoulalas-divanis-9783319981604","title":"Handbook of Mobile Data Privacy","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis handbook covers the fundamental principles and theory, and the state-of-the-art research, systems and applications, in the area of mobility data privacy. 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