2018· International Journal of Data Engineering and Intelligent Computing· 0 citations
TL;DR
A novel framework for integrating privacy-preserving ML techniques within Sovereign Cloud infrastructures is presented, combining cutting-edge cryptographic approaches with the data sovereignty features of Sovereign Clouds, ensuring data privacy, legal compliance, and efficient machine learning at scale.
Abstract
The rapid growth of machine learning (ML) technologies has raised concerns about the privacy and security of sensitive data used in training models. Privacy-preserving techniques such as federated learning, homomorphic encryption, and differential privacy are emerging solutions to protect data in ML applications. However, these techniques often face challenges in terms of scalability, performance, and compliance with data privacy regulations. Sovereign Cloud environments, characterized by strict data governance and jurisdictional controls, offer a potential solution for addressing these challenges. This paper presents a novel framework for integrating privacy-preserving ML techniques within Sovereign Cloud infrastructures. By combining cutting-edge cryptographic approaches with the data sovereignty features of Sovereign Clouds, our framework ensures data privacy, legal compliance, and efficient machine learning at scale. We discuss key challenges in data privacy, scalability, and legal compliance, and propose a set of best practices for deploying privacy-preserving ML in these environments. Additionally, we evaluate the proposed framework through case studies, demonstrating its potential in sectors such as healthcare and finance. The results show that our framework provides a balanced approach to privacy, scalability, and performance, contributing to the future of secure and responsible ML deployment.
This paper proposes a comprehensive framework for privacy-preserving feature engineering (PPFE) within federated learning analytics and explores techniques such as homomorphic encryption, differential privacy, and secure multi-party computation to enable robust, privacy-safe feature selection, transformation, and extraction across clients.
Yuki Nakamura, Olivia Martin· International Journal of Dat...· 0 citations
An in-depth analysis of federated learning methods and paying special attention to the issue of privacy is provided, which examines new developments, concerns and tradeoffs connected with privacy, effectiveness of communication, model noise, and scalability of systems.
Aarav Mehta· International Journal of App...· 0 citations
Today's digital age has made privacy and data protection a major concern-generally, with the kind of technologies that are turning things around and bringing everything to the cloud. FL will most likely provide a solution to the distance and make things clear in collaboration without exposing raw information from a consortium to boost its privacy. However, existing FL solutions include such challenges as increased overhead communication, risk in leaking data, and even the inefficiency of secure aggregation.To mitigate these constraints, this research proposes the Autoencoder-Based Federated Learning framework by integrating prevailing techniques such as differential privacy and homomorphic encryption that safeguard both the security and efficiency of the model. This method does not only steal model ideas for autoencoders to compress before sciences transmission but hugely reduces the transmission bandwidth and possibly minimizes gradient leakage. However, adaptive normalization is used to handle institutional heterogeneity to maintain better performance for the model. Conclusion of experimentation indicated that this framework could significantly reduce communication overhead while retaining high federated learning accuracy and even better security. Further, the trust-based client evaluation mechanism is presented to detect malicious behavior and improve reliability regarding federated aggregation. The experiment showed that Autoencoder Based Federated Learning was a scalable, secure, and privacy-efficient solution to applications tailored for healthcare, finance, and other sensitive data environments.
Guman Singh Chauhan, venkata Surya Teja Gollapalli, Kannan Srinivasan et al.· Journal of Science & Technol...· 0 citations
The research findings suggest that improved federated learning can achieve an optimal predictive performance, privacy protection, and secure collaborative learning, which makes it a viable method for next-generation distributed AI systems.
Sheetal Bawane, Leeladhar Chourasiya, S. Jain et al.· International journal of com...· 0 citations
This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination, and identifies promising directions for future research.
Shivendra Shukla, C. S. Gautam, Divyansh Tiwari· International Journal of Cre...· 0 citations
