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Books on the topic 'Privacy preserving machine learning'

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Author: Grafiati
Published: 10 December 2022
Last updated: 25 May 2024

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1

Li, Jin, Ping Li, Zheli Liu, Xiaofeng Chen, and Tong Li. Privacy-Preserving Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9139-3.

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2

Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4639-2.

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3

Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. New York, NY: Springer New York, 2013.

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4

Oyarzun Laura, Cristina, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg, et al., eds. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90874-4.

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5

Kim, Kwangjo, and Harry Chandra Tanuwidjaja. Privacy-Preserving Deep Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3764-3.

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6

Qu, Youyang, Longxiang Gao, Shui Yu, and Yong Xiang. Privacy Preservation in IoT: Machine Learning Approaches. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1797-4.

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7

Zimmeck, Sebastian. Using Machine Learning to improve Internet Privacy. [New York, N.Y.?]: [publisher not identified], 2017.

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8

Yu, Philip S. Machine Learning in Cyber Trust: Security, Privacy, and Reliability. Boston, MA: Springer-Verlag US, 2009.

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9

Lecuyer, Mathias. Security, Privacy, and Transparency Guarantees for Machine Learning Systems. [New York, N.Y.?]: [publisher not identified], 2019.

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10

Dimitrakakis, Christos, Aris Gkoulalas-Divanis, Aikaterini Mitrokotsa, Vassilios S. Verykios, and Yücel Saygin, eds. Privacy and Security Issues in Data Mining and Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19896-0.

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11

Dutta, Preetam Kumar. Machine Learning Based User Modeling for Enterprise Security and Privacy Risk Mitigation. [New York, N.Y.?]: [publisher not identified], 2019.

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12

Macintyre, John, Jinghua Zhao, and Xiaomeng Ma, eds. The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89508-2.

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13

Macintyre, John, Jinghua Zhao, and Xiaomeng Ma, eds. The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89511-2.

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14

MacIntyre, John, Jinghua Zhao, and Xiaomeng Ma, eds. The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62743-0.

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15

MacIntyre, John, Jinghua Zhao, and Xiaomeng Ma, eds. The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62746-1.

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16

Li, Jin, Tong Li, Xiaofeng Chen, Ping Li, and Zheli Liu. Privacy-Preserving Machine Learning. Springer Singapore Pte. Limited, 2022.

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17

Chang, J. Morris, Di Zhuang, and G. Dumindu Samaraweera. Privacy-Preserving Machine Learning. Manning Publications Co. LLC, 2022.

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18

Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2014.

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19

Kim, Kwangjo, and Harry Chandra Tanuwidjaja. Privacy-Preserving Deep Learning: A Comprehensive Survey. Springer Singapore Pte. Limited, 2021.

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20

Linguraru, Marius George, Cristina Oyarzun Laura, M. Jorge Cardoso, Michal Rosen-Zvi, and Georgios Kaissis. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning: 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings. Springer International Publishing AG, 2021.

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21

Correia, Anacleto, and Victor Lobo. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity. IGI Global, 2021.

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22

Lobo, Victor, and Anacleto Correia, eds. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9430-8.

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23

Correia, Anacleto, and Victor Lobo. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity. IGI Global, 2022.

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24

Correia, Anacleto, and Victor Lobo. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity. IGI Global, 2022.

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25

Correia, Anacleto, and Victor Lobo. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity. IGI Global, 2022.

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26

Correia, Anacleto, and Victor Lobo. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity. IGI Global, 2022.

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27

Tsai, Jeffrey J. P., and Philip S. Yu. Machine Learning in Cyber Trust: Security, Privacy, and Reliability. Springer, 2010.

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28

Tsai, Jeffrey J. P., and Philip S. Yu. Machine Learning in Cyber Trust: Security, Privacy, and Reliability. Springer, 2009.

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29

Sharma, Kavita, D. Jude Hemanth, Ramesh Chandra Poonia, and Yogita Gigras. Internet of Healthcare Things: Machine Learning for Security and Privacy. Wiley & Sons, Incorporated, John, 2021.

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30

Sharma, Kavita, D. Jude Hemanth, Ramesh Chandra Poonia, and Yogita Gigras. Internet of Healthcare Things: Machine Learning for Security and Privacy. Wiley & Sons, Incorporated, John, 2022.

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31

Sharma, Kavita, D. Jude Hemanth, Ramesh Chandra Poonia, and Yogita Gigras. Internet of Healthcare Things: Machine Learning for Security and Privacy. Wiley & Sons, Incorporated, John, 2021.

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32

Sharma, Kavita, D. Jude Hemanth, Ramesh Chandra Poonia, and Yogita Gigras. Internet of Healthcare Things: Machine Learning for Security and Privacy. Wiley & Sons, Incorporated, John, 2021.

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33

Xiang, Yong, Shui Yu, Longxiang Gao, and Youyang Qu. Privacy Preservation in IoT : Machine Learning Approaches: A Comprehensive Survey and Use Cases. Springer, 2022.

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34

Privacy and Security Issues in Data Mining and Machine Learning Lecture Notes in Artificial Intelligence. Springer, 2011.

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35

Zhao, Jinghua, John MacIntyre, and Xiaomeng Ma. 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy: SPIoT-2020, Volume 2. Springer International Publishing AG, 2020.

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36

Zhao, Jinghua, John Macintyre, and Xiaomeng Ma. 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy: SPIoT-2021 Volume 2. Springer International Publishing AG, 2021.

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37

Zhao, Jinghua, John Macintyre, and Xiaomeng Ma. 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy: SPIoT-2021 Volume 1. Springer International Publishing AG, 2021.

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38

Zhao, Jinghua, John MacIntyre, and Xiaomeng Ma. 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy: SPIoT-2020, Volume 1. Springer International Publishing AG, 2020.

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39

Dimitrakakis, Christos, Aris Gkoulalas-Divanis, Aikaterini Mitrokotsa, Vassilios S. Verykios, and Yücel Saygin. Privacy and Security Issues in Data Mining and Machine Learning: International ECML/PKDD Workshop, PSDML 2010, Barcelona, Spain, September 24, 2010. Revised Selected Papers. Springer, 2011.

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40

Cowhey, Peter F., and Jonathan D. Aronson. Information and Production Disruptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190657932.003.0002.

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Digital technology (digital DNA) has catalyzed information and production disruptions—ranging from cloud computing and machine learning to 3D printers—that are sweeping across firms and markets. Special attention is paid to competition dynamics in digital markets and the creation of a trusted digital environment for digital privacy and cybersecurity. A new system of innovation, digital platform clusters, is emerging that will change both global industrial and regional growth patterns, even in traditional industries like farming. Discussions of the automotive and electric grid industries illustrate the new dynamics. The fate of this new innovation system will depend on making smart choices about national and global economic governance.
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41

Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.
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42

Baecker, Ronald M. Computers and Society. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198827085.001.0001.

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The last century has seen enormous leaps in the development of digital technologies, and most aspects of modern life have changed significantly with their widespread availability and use. Technology at various scales - supercomputers, corporate networks, desktop and laptop computers, the internet, tablets, mobile phones, and processors that are hidden in everyday devices and are so small you can barely see them with the naked eye - all pervade our world in a major way. Computers and Society: Modern Perspectives is a wide-ranging and comprehensive textbook that critically assesses the global technical achievements in digital technologies and how are they are applied in media; education and learning; medicine and health; free speech, democracy, and government; and war and peace. Ronald M. Baecker reviews critical ethical issues raised by computers, such as digital inclusion, security, safety, privacy,automation, and work, and discusses social, political, and ethical controversies and choices now faced by society. Particular attention is paid to new and exciting developments in artificial intelligence and machine learning, and the issues that have arisen from our complex relationship with AI.
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43

Allen, Hilary J. Driverless Finance. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197626801.001.0001.

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Everyone is talking about fintech, and they’re usually saying good things. Driverless Finance provides a balance to that conversation, exploring the threats that different fintech innovations pose for our financial system. With in-depth and accessible descriptions of new financial technologies and business models—ranging from distributed ledgers to machine learning, cryptoassets to robo-investing—this book helps readers to think more critically about fintech, and about how the law should respond to it. This book highlights the increased speed, complexity, and coordination inherent in new fintech innovations, and shows how these features could come together in a massive financial system failure. It makes the case for a precautionary approach to regulating fintech, erring on the side of caution to avoid a financial crisis that could have irreversible and catastrophic effects for our society. Because fintech’s system risks aren’t fully addressed by existing financial regulation (or by experimental new approaches like regulatory sandboxes), this book makes several bold new proposals for regulation designed to make fintech-inspired financial crises less likely. These proposals include new forms of disclosure and supervision, new forms of technological tools (known as suptech), and a new licensing regime for financial technologies. This book finishes by discussing how fintech’s impact on financial stability relates to pressing debates about innovation, expertise, cybersecurity, privacy, and competition.
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