Review on Privacy Preservation Techniques and Challenges in IoT Devices

Author:

Meena PrakashORCID,Jajal BrijeshORCID,Khanna SamratORCID

Publisher

Springer Nature Switzerland

Reference9 articles.

1. Khan, F., Rehman, A.U., Zheng, J., Jan, M.A., Alam, M.: Mobile crowdsensing: a survey on privacy-preservation, task management, assignment models, and incentives mechanisms. Future Gener. Comput. Syst. 100, 456–472 (2019). https://doi.org/10.1016/j.future.2019.02.014

2. Akil, M., Islami, L., Fischer-Hubner, S., Martucci, L.A., Zuccato, A.: Privacy-preserving identifiers for IoT: a systematic literature review. IEEE Access 8, 168470–168485 (2020). https://doi.org/10.1109/ACCESS.2020.3023659

3. Lei, Y., Ye, D., Shen, S., Sui, Y., Zhu, T., Zhou, W.: New Challenges in Reinforcement Learning: A Survey of Security and Privacy”, Springer Nature 2021, arXiv:2301.00188v1 [cs.LG] 31 Dec 2022

4. Rachel, C., et al.: “Challenges towards the Next Frontier in Privacy” April 17, 2023. arXiv:2304.06929v1 [cs.CR] 14 Apr 2023

5. Majid, A.: Security and privacy concerns over IoT devices attacks in smart cities (2022). J. Comput. Commun. 11, 26–42 (2023). https://www.scirp.org/journal/jcc. ISSN Online: 2327-5227 ISSN Print: 2327-5219

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