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Compressed Sensing for Privacy-Preserving Data Processing

  • Matteo Testa
  • Diego Valsesia
  • Tiziano Bianchi
  • Enrico Magli

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Also part of the SpringerBriefs in Signal Processing book sub series (BRIEFSSIGNAL)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Matteo Testa, Diego Valsesia, Tiziano Bianchi, Enrico Magli
    Pages 1-6
  3. Matteo Testa, Diego Valsesia, Tiziano Bianchi, Enrico Magli
    Pages 7-24
  4. Matteo Testa, Diego Valsesia, Tiziano Bianchi, Enrico Magli
    Pages 25-71
  5. Matteo Testa, Diego Valsesia, Tiziano Bianchi, Enrico Magli
    Pages 73-90
  6. Matteo Testa, Diego Valsesia, Tiziano Bianchi, Enrico Magli
    Pages 91-91

About this book

Introduction

The objective of this book is to provide the reader with a comprehensive survey of the topic compressed sensing in information retrieval and signal detection with privacy preserving functionality without compromising the performance of the embedding in terms of accuracy or computational efficiency. The reader is guided in exploring the topic by first establishing a shared knowledge about compressed sensing and how it is used nowadays. Then, clear models and definitions for its use as a cryptosystem and a privacy-preserving embedding are laid down, before tackling state-of-the-art results for both applications. The reader will conclude the book having learned that the current results in terms of security of compressed techniques allow it to be a very promising solution to many practical problems of interest. The book caters to a broad audience among researchers, scientists, or engineers with very diverse backgrounds, having interests in security, cryptography and privacy in information retrieval systems. Accompanying software is made available on the authors’ website to reproduce the experiments and techniques presented in the book. The only background required to the reader is a good knowledge of linear algebra, probability and information theory.

Keywords

cryptosystem models signal embedding dimensionality reduction Internet-of-Things privacy preserving information retrieval data security data privacy random matrices circulant matrices energy abfuscation random matrix theory compressed sensing

Authors and affiliations

  • Matteo Testa
    • 1
  • Diego Valsesia
    • 2
  • Tiziano Bianchi
    • 3
  • Enrico Magli
    • 4
  1. 1.Department of Electronics and TelecommunicationsPolitecnico di TorinoTurinItaly
  2. 2.Department of Electronics and TelecommunicationsPolitecnico di TorinoTurinItaly
  3. 3.Department of Electronics and TelecommunicationsPolitecnico di TorinoTurinItaly
  4. 4.Department of Electronics and TelecommunicationsPolitecnico di TorinoTurinItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-13-2279-2
  • Copyright Information The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-13-2278-5
  • Online ISBN 978-981-13-2279-2
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site
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