Liu A Algorithms for Data and Computation Privacy 2021 torrent

Textbook in PDF format

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 –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.
Range Queries over Encrypted Data
Fast and Scalable Range and Keyword Query Processing Over Encrypted Data with Provable Adaptive Security
Nearest Neighbor Queries over Encrypted Data
K-Nearest Neighbor Queries Over Encrypted Data
Top-k Queries for Two-Tiered Sensor Networks
Collaborative Enforcement of Firewall Policies in Virtual Private Networks
Privacy Preserving Quantification of Cross-Domain Network Reachability
Cross-Domain Privacy-Preserving Cooperative Firewall Optimization
Privacy Preserving String Matching for Cloud Computing
Privacy Preserving Information Hub Identification in Social Networks
Publishing Social Network Data with Privacy Guarantees
Predictable Privacy-Preserving Mobile Crowd Sensing
Differentially Private and Budget Limited Bandit Learning over Matroids
Breaching Privacy in Encrypted Instant Messaging Networks