Algorithms for Data and Computation Privacy / by Alex X. Liu, Rui Li.

Format:

Book

Author/Creator:

Liu, Alex X., Author.

Li, Rui, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Data protection-Law and legislation.

Computer networks.

Information storage and retrieval systems.

Computer networks-Security measures.

Data protection.

Privacy.

Computer Communication Networks.

Information Storage and Retrieval.

Mobile and Network Security.

Data and Information Security.

Local Subjects:

Privacy.

Computer Communication Networks.

Information Storage and Retrieval.

Mobile and Network Security.

Data and Information Security.

Physical Description:

1 online resource (XVIII, 404 pages) : 163 illustrations, 130 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

Part I Privacy Preserving Queries

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

Part II Privacy Preserving Computation

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

Part III Differential Privacy

Publishing Social Network Data with Privacy Guarantees

Predictable Privacy-Preserving Mobile Crowd Sensing

Differentially Private and Budget Limited Bandit Learning over Matroids

Part IV Breaking Privacy

Breaching Privacy in Encrypted Instant Messaging Networks.

Other Format:

Printed edition:

ISBN:

978-3-030-58896-0

9783030588960

Access Restriction:

Restricted for use by site license.