Modelling Data Privacy and Security with Respect to Machine Learning

Authors

  • K S Dhruva Teja  Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Bangalore, India
  • K. Vamshivardhan  Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Bangalore, India
  • K. Partha Sai  Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Bangalore, India
  • D. Hemanth  Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Bangalore, India

DOI:

https://doi.org/10.32628/IJSRSET229633

Keywords:

Big Data, Security, Privacy, Machine Learning, Artificial Intelligence.

Abstract

Organizations are collecting larger amounts of data to build complex data analytics, machine learning and AI models. Furthermore, the data needed for building such models may be unstructured (e.g., text, image, and video). Hence such data may be stored in different data management systems ranging from relational databases to newer NoSQL databases tailored for storing unstructured data. In some cases, the developed code will be automatically executed by the NoSQL system on the stored data. These developments indicate the need for a data security and privacy solution that can uniformly protect data stored in many different data management systems and enforce security policies even if sensitive data is processed using a data scientist submitted complex program.

References

  1. Zhang, P. Porambage et al., “Sec-EdgeAI: AI for Edge Security Vs Security for Edge AI,” 1st 6G Wireless Summit, 2019.
  2. Regulation (EU) 2016/679 of the European Parliament and of the Council, General Data Protection Regulation (GDPR).
  3. California Consumer Privacy Act of 2018 (CCPA).
  4. European Commission White Paper “On  Artificial Intelligence – A European approach to excellence and trust”, Brussels, 19.2.2020 COM(2020) 65 Final, Available from: https://ec.europa.eu/info/sites/info/files/commiss commission-white-paper-artificialintelligence-feb2020_en.pdf, last accessed March 2020.
  5. Charter of Fundamental Rights of the European Union 2012/C 326/02.
  6. C. Fennessy, “US sens. Unveil new federal provacy legislation”, Nov 26, 2019, International Association of Privacy Professionals (IAPP), Available from: https://iapp.org/news/a/u-s-senators-unveil-new-federal-privacylegislation/, last accessed March 2020.
  7. 2019 Consumer Data Privacy Legislation, National Conference of State Legislatures, 2019 available https://www.ncsl.org/research/telecommunications-and-informationtechnology/consumer-data-privacy.aspx, last accessed March 2022

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Published

2022-12-30

Issue

Section

Research Articles

How to Cite

[1]
K S Dhruva Teja, K. Vamshivardhan, K. Partha Sai, D. Hemanth "Modelling Data Privacy and Security with Respect to Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 6, pp.235-238, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRSET229633