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Privacy-Preserving Framework for Deep Learning Cybersecurity Solutions

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Education, Research and Business Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 276))

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Abstract

Experimentation with artificial intelligence (AI) and machine learning (ML) has rapidly advanced in the last decade, mainly due to the progress made in data storage and processing technologies. As this technology is implemented in everyday devices, ranging from smart appliances to personal assistants, we observe an increase in interest in more rigid to change sectors, like governmental and military. Utilizing a machine learning service is a commodity for today’s society, introducing data privacy challenges for data owners and security concerns for model developers. As the EU plans to build a network of AI-enabled Security Operations Centers across Europe and NATO believes in including artificial intelligence in its decision-making process, we need to focus intensely on the security and privacy offered by machine learning-based applications. This paper contains our summarization of the current concerns regarding privacy-preserving machine learning and an analysis of the present frameworks highlighting possible attacks and methodology. Additionally, we present our approach as a potential solution for privacy-preserving models used in cybersecurity applications. Our research shows that even though all three methods guarantee privacy at a certain level, a holistic approach proves to be more efficient.

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Authors and Affiliations

  1. Military Technical Academy “Ferdinand I”, 050141, Bucharest, Romania

    Constantin Nilă, Marius Preda & Victor Patriciu

Authors
  1. Constantin Nilă
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  2. Marius Preda
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  3. Victor Patriciu
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Corresponding author

Correspondence to Constantin Nilă .

Editor information

Editors and Affiliations

  1. Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania

    Cristian Ciurea

  2. Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania

    Cătălin Boja

  3. Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania

    Paul Pocatilu

  4. Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania

    Mihai Doinea

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Nilă, C., Preda, M., Patriciu, V. (2022). Privacy-Preserving Framework for Deep Learning Cybersecurity Solutions. In: Ciurea, C., Boja, C., Pocatilu, P., Doinea, M. (eds) Education, Research and Business Technologies. Smart Innovation, Systems and Technologies, vol 276. Springer, Singapore. https://doi.org/10.1007/978-981-16-8866-9_18

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