Privacy Preservation for Enterprises Data in Edge Devices
Keywords:Privacy Preservation, edge devices, Anonymization, Data security, Enterprise Data protection
Privacy becomes the most important topic as user’s data gets more and more widely used and exchanged across internet. Edge devices are replacing traditional monitoring and maintenance strategy for daily used items in households as well as industrial establishments. The usage of technology is getting more and more pervasive. 6G further increases the importance of edge devices in a network as network speeds increase, making the edge device much more powerful element in the network. Edge devices would have massive store and exchange of personal data of the individual. Data privacy forms the primary requirement for accessing data of individuals. Paper presents a novel concept on combination of techniques including cryptography, randomization, pseudonymization and others to achieve anonymization. It investigates in detail how the privacy relevant data of individuals can be protected as well as made relevant for research. It arrives at an interesting and unique approach for privacy preservation on edge devices opening up new business opportunities and make the data subject in charge of their data.
A. Westin, “Privacy And Freedom,” Wash. Lee Law Rev., vol. 25, no. 1, p. 166, Mar. 1968.
T. Takenaka, Y. Yamamoto, K. Fukuda, A. Kimura, and K. Ueda, “Enhancing products and services using smart appliance networks,” CIRP Ann., vol. 65, no. 1, pp. 397–400, Jan. 2016, doi: 10.1016/j.cirp.2016.04.062.
A. K. Jain and B. B. Gupta, “A survey of phishing attack techniques, defence mechanisms and open research challenges,” Enterp. Inf. Syst., vol. 0, no. 0, pp. 1–39, Mar. 2021, doi: 10.1080/17517575.2021.1896786.
“Automatically Granted Permissions in Android apps |Proceedings of the 17th International Conference on Mining Software Repositories.” https://dl.acm.org/doi/abs/10.1145/3379597.3387469 (accessed Nov. 29, 2020).
“Tackling Urban Mobility with Technology,” Google Europe Blog. https://europe.googleblog.com/2015/11/tackling-urban-mobility-with-technology.html (accessed Dec. 18, 2019).
J. Zhao, R. Mortier, J. Crowcroft, and L. Wang, “Privacy-Preserving Machine Learning Based Data Analytics on Edge Devices,” in Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, New York, NY, USA, Dec. 2018, pp. 341–346. doi: 10.1145/3278721.3278778.
X. Lu, Y. Liao, P. Lio, and P. Hui, “Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing,” IEEE Access, vol. 8, pp. 48970–48981, 2020, doi: 10.1109/ACCESS.2020.2978082.
A. Anant and R. Prasad, “State-of-the-art in Privacy Preservation for Enterprise Data,” in 2020 23rd International Symposium on Wireless Personal Multimedia Communications (WPMC), Oct. 2020, pp. 1–6. doi: 10.1109/WPMC50192.2020.9309459.
S. L. Garfinkel, J. M. Abowd, and S. Powazek, “Issues Encountered Deploying Differential Privacy,” in Proceedings of the 2018 Workshop on Privacy in the Electronic Society, New York, NY, USA, Jan. 2018, pp. 133–137. doi: 10.1145/3267323.3268949.
“Titanic - Machine Learning from Disaster.” https://kaggle.com/c/titanic (accessed Dec. 30, 2020).
L. Sweeney, A. Abu, and J. Winn, “Identifying Participants in the Personal Genome Project by Name (A Re-identification Experiment),” ArXiv13047605 Cs, Apr. 2013, Accessed: Apr. 07, 2021. [Online]. Available: http://arxiv.org/abs/1304.7605
“Introduction to the Mobile Security Testing Guide.” https://mobile-security.gitbook.io/mobile-security-testing-guide/overview/0x03-overview (accessed Apr. 08, 2021).
S.-A. Elvy, “Paying for Privacy and the Personal Data Economy,” Columbia Law Rev., vol. 117, p. 1369, 2017.
“CC 235.1 Federal Act of 19 June 1992 on Data Protection (FADP).” https://www.admin.ch/opc/en/classified-compilation/19920153/index.html (accessed Dec. 30, 2020).
A. Anant and R. Prasad, “Data Privacy Technology for Society,” River Publ., vol. Series in Information Science and Technology, p. 16, doi: 10.13052/rp-9788770222174.
H. Wen, Q. Zhao, Z. Lin, D. Xuan, and N. Shroff, “A Study of the Privacy of COVID-19 Contact Tracing Apps,” in Security and Privacy in Communication Networks, Cham, 2020, pp. 297–317. doi: 10.1007/978-3-030-63086-7_17.