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Compressed Sensing for Privacy-Preserving Data Processing

Part of the SpringerBriefs in Electrical and Computer Engineering Book Series

DOI: 10.1007/978-981-13-2279-2

ISBN: 978-981-13-2278-5

Publisher: Springer Singapore - Springer Journals

Sep 2, 2018

Available

Introduction

Testa, Matteo; Valsesia, Diego; Bianchi, Tiziano; Magli, Enrico

Pages: 1 –6

[Information processing systems have been revolutionized by recent advances in several technological areas, like device miniaturization, wireless transmission, network infrastructure. Traditional information sources have been replaced by a multitude of devices with sensing capabilities, the...
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Compressed Sensing and Security

Testa, Matteo; Valsesia, Diego; Bianchi, Tiziano; Magli, Enrico

Pages: 7 –24

[In this chapter we briefly review the Compressed Sensing (CS) framework, discussing the acquisition model, the conditions under which the signal can be recovered, and the main reconstruction algorithms. Then, we show how CS is essentially analogous to a private key cryptosystem if signal...
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Compressed Sensing as a Cryptosystem

Testa, Matteo; Valsesia, Diego; Bianchi, Tiziano; Magli, Enrico

Pages: 25 –71

[This chapter presents the most relevant results on Compressed Sensing (CS) used as a cryptosystem. First, we analyze the statistical properties of CS measurements, showing that they always convey at least the energy of the sensed signal. Then, we discuss the secrecy achievable by different...
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Privacy-Preserving Embeddings

Testa, Matteo; Valsesia, Diego; Bianchi, Tiziano; Magli, Enrico

Pages: 73 –90

[In this chapter, we illustrate main results on privacy-preserving embeddings. Here, security properties of embeddings are analyzed by considering two possible scenarios for their use. In the first case, a client submits a query containing sensitive information to a server, which should respond...
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Conclusions

Testa, Matteo; Valsesia, Diego; Bianchi, Tiziano; Magli, Enrico

Pages: 91 –91

[Compressed sensing has drawn remarkable attention because of its appealing compress-while-sampling property. Nonetheless, its noteworthy properties as privacy-preserving framework are among its lesser known aspects. This book enlightened this latter perspective by summarizing state-of-the-art...

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