Strategies for Data Privacy in Telecommunication Systems

Authors

  • Sri Nikhil Annam   Independent Researcher, USA

DOI:

https://doi.org/10.32628/CSEIT2390681

Keywords:

Data Privacy in Telecommunication Systems, Encryption, Cybersecurity, Regulation Compliance, Homomorphic Encryption, Blockchain, Differential Privacy, Federated Learning

Abstract

This research paper discusses the many strategies used to protect data privacy within telecommunication systems. As the industry becomes increasingly data-driven, effective measures to protect sensitive user information from growing cybersecurity risks are necessary. This paper sheds light on the evolution of data privacy, current challenges, regulatory frameworks, and advanced technological strategies. It culminates in some insight into emerging innovations promising future improvement in data privacy.

References

  1. Aono, Y., et al. (2018). Privacy-preserving federated learning using homomorphic encryption. MDPI.
  2. Dowlin, D., et al. (2017). Neural networks based on homomorphic encryption for privacy-preserving machine learning. IEEE Xplore.
  3. Dowlin, D., et al. (2017). Secure and privacy-preserving machine learning algorithms using homomorphic encryption. IEEE Transactions on Information Forensics and Security.
  4. Froelicher, T., et al. (2019). Privacy-preserving federated learning using the ElGamal elliptic curve cryptosystem. Journal of Cryptographic Engineering.
  5. Geyer, R. C., et al. (2018). Differential privacy in federated learning: Privacy preservation in distributed machine learning. IEEE Transactions on Neural Networks and Learning Systems.
  6. Homomorphic, J., & Encryption, D. (2018). Federated learning: A new frontier for secure machine learning. Proceedings of the 2018 International Conference on Machine Learning.
  7. Kang, J., & Wang, Y. (2018). Securing federated learning with homomorphic encryption. Proceedings of the IEEE International Conference on Cloud Computing.
  8. Kim, J., et al. (2017). Homomorphic encryption for privacy-preserving deep learning. Journal of Privacy and Confidentiality.
  9. Li, Y., et al. (2020). Blockchain-based federated learning for privacy-preserving data sharing. Journal of Computational Science.
  10. Liu, Z., & Li, M. (2020). Differential privacy in blockchain and federated learning for privacy protection. IEEE Transactions on Information Theory.
  11. McMahan, B., et al. (2018). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics.
  12. Park, J., & Lim, H. (2022). Privacy-preserving federated learning using homomorphic encryption. MDPI.
  13. Wang, H., et al. (2019). A survey of federated learning in privacy-preserving machine learning. Journal of Privacy and Data Security.
  14. Wu, J., et al. (2019). Advanced encryption methods for secure federated learning. IEEE Cloud Computing.
  15. Wu, Y., et al. (2020). Secure federated learning in telecommunications using blockchain and homomorphic encryption. IEEE Journal on Selected Areas in Communications.
  16. Xu, X., et al. (2018). Blockchain-based federated learning with enhanced privacy and security. IEEE Transactions on Industrial Informatics.
  17. Zhang, L., et al. (2019). Federated learning and privacy: Challenges and solutions. IEEE Access.
  18. Zhang, S., et al. (2019). Blockchain for privacy-preserving federated learning in IIoT. IEEE Internet of Things Journal.
  19. Zhang, Y., et al. (2017). Ensuring privacy in federated learning systems with homomorphic encryption. International Journal of Computer Applications.
  20. Zheng, X., et al. (2020). Homomorphic encryption-based federated learning with security and privacy guarantees. ACM Computing Surveys.

Downloads

Published

2023-11-30

Issue

Section

Research Articles

How to Cite

[1]
Sri Nikhil Annam , " Strategies for Data Privacy in Telecommunication Systems" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.397-406, November-December-2023. Available at doi : https://doi.org/10.32628/CSEIT2390681