Название: Algorithms for Data and Computation Privacy Автор: Alex X. Liu, Rui Li Издательство: Springer Год: 2021 Формат: True PDF Страниц: 412 Размер: 10 Mb Язык: English
This book introduces the state-of-the-art algorithms for data and computation privacy. It mainly focuses on searchable symmetric encryption algorithms and privacy preserving multi-party computation algorithms. This book also introduces algorithms for breaking privacy, and gives intuition on how to design algorithm to counter privacy attacks. Some well-designed differential privacy algorithms are also included in this book. Driven by lower cost, higher reliability, better performance, and faster deployment, data and computing services are increasingly outsourced to clouds. In this computing paradigm, one often has to store privacy sensitive data at parties, that cannot fully trust and perform privacy sensitive computation with parties that again cannot fully trust. For both scenarios, preserving data privacy and computation privacy is extremely important. After the Facebook–Cambridge Analytical data scandal and the implementation of the General Data Protection Regulation by European Union, users are becoming more privacy aware and more concerned with their privacy in this digital world. This book targets database engineers, cloud computing engineers and researchers working in this field. Advanced-level students studying computer science and electrical engineering will also find this book useful as a reference or secondary text.
Handbook of Mobile Data Privacy Название: Handbook of Mobile Data Privacy Автор: Aris Gkoulalas-Divanis Издательство: Springer Год: 2018 Страниц: 403 Формат: PDF, EPUB Размер: 48 Mb...
Differential Privacy and Applications Название: Differential Privacy and Applications Автор: Tianqing Zhu and Gang Li Издательство: Springer Год: 2017 Формат: PDF Размер: 10 Мб Язык:...
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Algorithms for Data and Computation Privacy 
