Decentralization and Federated Approach for Personal Data Protection and Privacy Control

Singh, Suyogita; Verma, Satya Bhushan; Gupta, Bineet Kumar; Agrawal, Anamika

doi:10.2478/ias-2024-0014

Abstract

As the digital ecosystem evolves, personal data protection and privacy control have become paramount concerns. Traditional centralized approaches to data management and privacy often pose risks to individuals’ data security and privacy. This research paper explores the potential of decentralization and federated approaches as innovative solutions for enhancing personal data protection and privacy control. By distributing data across multiple entities and empowering individuals with control over their data, decentralized and federated models offer increased security, privacy, and data autonomy. This paper investigates the benefits, challenges, and future prospects of decentralized and federated approaches in personal data protection and privacy control.

References

Khalid, U., Asim, M., Baker, T., Hung, P.C.K., Tariq, M.A. and Rafferty, L. (2020). ‘A decentralized lightweight blockchain-based authentication mechanism for IoT systems’. Cluster Computing, 23(3), pp.2067-2087.
Search in Google Scholar Back to article
Nakamoto, S. (2008). ‘Re: bitcoin P2P e-cash paper’. Cryptography Mailing List.
Search in Google Scholar Back to article
Shen, H., Zhang, M., Wang, H., Guo, F. and Susilo, W. (2021). ‘A cloud-aided privacy-preserving multi-dimensional data comparison protocol’. Information Sciences, 545, pp.739-752.
Search in Google Scholar Back to article
Yang, Q., Liu, Y., Chen, T. and Tong, Y. (2019). ‘Federated machine learning: concept and applications’. ACM Transactions on Intelligent Systems and Technology, 10(2), pp.1-19.
Search in Google Scholar Back to article
Wang, P., Wang, L., Leung, H. and Zhang, G. (2020). ‘Super-resolution mapping based on spatial– spectral correlation for spectral imagery’. IEEE Transactions on Geoscience and Remote Sensing, 59(3), pp.2256-2268.
Search in Google Scholar Back to article
Zhou, W., Lv, Y., Lei, J. and Yu, L. (2019). ‘Global and local-contrast guides content-aware fusion for RGB-D saliency prediction’. IEEE Transactions on Systems, Man, and Cybernetics: Systems.
Search in Google Scholar Back to article
Konecný, J. et al. (2016). ‘Federated learning: strategies for improving communication efficiency’. CoRR, abs/1610.05492. Available at: http://arxiv.org/abs/1610.05492.
Search in Google Scholar Back to article
Zhou, S. et al. (2019). ‘PIRATE: a blockchain-based secure framework of distributed machine learning in 5G networks’. CoRR, abs/1912.07860. Available at: http://arxiv.org/abs/1912.07860.
Search in Google Scholar Back to article
Khan, L.U., Saad, W., Han, Z., Hossain, E. and Hong, C.S. (2021). ‘Federated learning for internet of things: recent advances, taxonomy, and open challenges’. IEEE Communications Surveys & Tutorials.
Search in Google Scholar Back to article
Chen, X. (2018). ‘When machine learning meets blockchain: a decentralized, privacy-preserving and secure design’. In: Proceedings of the 2018 IEEE International Conference on Big Data (Big Data).
Search in Google Scholar Back to article
Hegedüs, I., Danner, G. and Jelasity, M. (2019). ‘Gossip learning as a decentralized alternative to federated learning’. In: Proceedings of the 2019 International Conference on Distributed Applications and Interoperable Systems (DAIS), 19th IFIP WG 6.1.
Search in Google Scholar Back to article
Li, T., Sahu, A.K., Talwalkar, A. and Smith, V. (2020). ‘Federated learning: challenges, methods, and future directions’. IEEE Signal Processing Magazine, 37.
Search in Google Scholar Back to article
Kairouz, P., McMahan, H.B. and Avent, B. (2019). ‘Advances and open problems in federated learning’. [Online].
Search in Google Scholar Back to article
Gai, A., Wu, Y., Zhu, L., Zhang, Z. and Qiu, M. (2020). ‘Differential privacy-based blockchain for industrial internet-of-things’. IEEE Transactions on Industrial Informatics.
Search in Google Scholar Back to article
Kim, H., Park, J., Bennis, M. and Kim, S. (2020). ‘Blockchained on-device federated learning’. IEEE Communications Letters.
Search in Google Scholar Back to article
Liu, Y., Peng, J., Kang, J., Iliyasu, A.M., Niyato, D. and ElLatif, A.A.A. (2020). ‘A secure federated learning framework for 5G networks’. IEEE Wireless Communications.
Search in Google Scholar Back to article
Ma, L., Pei, Q., Qu, Y., Fan, K. and Lai, X. (2019). ‘Decentralized privacy-preserving reputation management for mobile crowdsensing’. In: International Conference on Security and Privacy in Communication Systems. Springer.
Search in Google Scholar Back to article
Jiao, Y., Wang, P., Niyato, D. and Suankaewmanee, K. (2019). ‘Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks’. IEEE Transactions on Parallel and Distributed Systems.
Search in Google Scholar Back to article
Konecný, J., McMahan, H.B., Ramage, D. and Richt, P. (2016). ‘Federated optimization: distributed machine learning for on-device intelligence’. CoRR.
Search in Google Scholar Back to article
Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X. and Chen, M. (2019). ‘In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning’. IEEE Network.
Search in Google Scholar Back to article
Smith, V., Chiang, C., Sanjabi, M. and Talwalkar, A.S. (2017). ‘Federated multi-task learning’. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems.
Search in Google Scholar Back to article
Konecný, J., McMahan, H.B., Yu, F.X., Richt, P., Suresh, A.T. and Bacon, D. (2016). ‘Federated learning: strategies for improving communication efficiency’. CoRR.
Search in Google Scholar Back to article
Nishio, T. and Yonetani, R. (2019). ‘Client selection for federated learning with heterogeneous resources in mobile edge’. In: 2019 IEEE International Conference on Communications.
Search in Google Scholar Back to article
Lu, Y., Huang, X., Dai, Y., Maharjan, S. and Zhang, Y. (2019). ‘Blockchain and federated learning for privacy-preserved data sharing in industrial IoT’. IEEE Transactions on Industrial Informatics.
Search in Google Scholar Back to article
Yoon, J. (2019). ‘Democratic senators introduce the Consumer Online Privacy Rights Act’. [Internet].
Search in Google Scholar Back to article
Aktypi, A., Nurse, J.R.C. and Goldsmith, M. (2017). ‘Unwinding Ariadne’s identity thread: privacy risks with fitness trackers and online social networks’. In: International Workshop on Multimedia Privacy and Security at ACM Conference on Computer and Communications Security.
Search in Google Scholar Back to article
Mehmood, A., Natgunanathan, I., Xiang, Y., Hua, G. and Guo, S. (2016). ‘Protection of big data privacy’. IEEE Access, 4.
Search in Google Scholar Back to article
Saglam, B.R. and Nurse, J.R.C. (2020). ‘Is your chatbot GDPR compliant? Open issues in agent design’. In: International Conference on Conversational User Interfaces. ACM.
Search in Google Scholar Back to article
Ramachandran, G.S., Radhakrishnan, R. and Krishnamachari, B. (2018). ‘Towards a decentralized data marketplace for smart cities’. In: International Smart Cities Conference. IEEE.
Search in Google Scholar Back to article
Lönnfält, I. and Sandqvist, J. (2018). ‘Blockchains, the new fashion in supply chains? The compatibility of blockchain configuration in supply chain management in the fast fashion industry’. (Master’s thesis), Gothenburg University.
Search in Google Scholar Back to article
Rumbold, J.M. and Pierscionek, B.K. (2018). ‘What are data? A categorization of the data sensitivity spectrum’. Big Data Research, 12.
Search in Google Scholar Back to article
Purtova, N. (2018). ‘The law of everything: broad concept of personal data and future of EU data protection law’. Innov Technology.
Search in Google Scholar Back to article
Singh, S. and Verma, S. (2023). ‘Federated learning for GDPR’. In: The 18th International Conference on Information Assurance and Security (IAS). Springer, December 13-15, 2023.
Search in Google Scholar Back to article
Verma, S.B. and Shashi, B.V. (2020). ‘Data transmission in BPEL (Business Process Execution Language)’. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(3), pp.105-117.
Search in Google Scholar Back to article
Verma, S.B., Brijesh, P. and Gupta, B.K. (2022). ‘Containerization and its architectures: a study’. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), pp.395-409.
Search in Google Scholar Back to article

Decentralization and Federated Approach for Personal Data Protection and Privacy Control

Abstract

Paradigm

My account