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Emerging Technologies in a Modern Competitive Scenario: Understanding the Panorama for Security and Privacy Requirements

Emerging Technologies in a Modern Competitive Scenario: Understanding the Panorama for Security and Privacy Requirements

George Leal Jamil, Alexis Rocha da Silva
Copyright: © 2021 |Pages: 16
ISBN13: 9781799842019|ISBN10: 1799842010|ISBN13 Softcover: 9781799858485|EISBN13: 9781799842026
DOI: 10.4018/978-1-7998-4201-9.ch001
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MLA

Jamil, George Leal, and Alexis Rocha da Silva. "Emerging Technologies in a Modern Competitive Scenario: Understanding the Panorama for Security and Privacy Requirements." Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy, edited by Pedro Fernandes Anunciação, et al., IGI Global, 2021, pp. 1-16. https://doi.org/10.4018/978-1-7998-4201-9.ch001

APA

Jamil, G. L. & Rocha da Silva, A. (2021). Emerging Technologies in a Modern Competitive Scenario: Understanding the Panorama for Security and Privacy Requirements. In P. Anunciação, C. Pessoa, & G. Jamil (Eds.), Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy (pp. 1-16). IGI Global. https://doi.org/10.4018/978-1-7998-4201-9.ch001

Chicago

Jamil, George Leal, and Alexis Rocha da Silva. "Emerging Technologies in a Modern Competitive Scenario: Understanding the Panorama for Security and Privacy Requirements." In Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy, edited by Pedro Fernandes Anunciação, Cláudio Roberto Magalhães Pessoa, and George Leal Jamil, 1-16. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4201-9.ch001

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Abstract

Users' personal, highly sensitive data such as photos and voice recordings are kept indefinitely by the companies that collect it. Users can neither delete nor restrict the purposes for which it is used. Learning how to machine learning that protects privacy, we can make a huge difference in solving many social issues like curing disease, etc. Deep neural networks are susceptible to various inference attacks as they remember information about their training data. In this chapter, the authors introduce differential privacy, which ensures that different kinds of statistical analysis don't compromise privacy and federated learning, training a machine learning model on a data to which we do not have access to.

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