Vol. 4 No. 2 (2024): July−December 2024
Articles

Privacy-Preserving AI Analytics in Cloud Computing: A Federated Learning Approach for Cross-Organizational Data Collaboration

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  • Jiayan Fan,
  • Haisheng Lian,
  • Wenbo Liu
Jiayan Fan
Information Science, University of Michigan, MI, USA
Haisheng Lian
Material Physics, Sun Yat-sen University, Guangzhou, China
Wenbo Liu
Northeastern University, Software Engineering, MA, USA

Published 2024-11-20

Keywords

  • Privacy-Preserving Machine Learning,
  • Federated Learning,
  • Cross-Organizational Data Collaboration,
  • Homomorphic Encryption

How to Cite

Fan, J., Lian, H., & Liu, W. (2024). Privacy-Preserving AI Analytics in Cloud Computing: A Federated Learning Approach for Cross-Organizational Data Collaboration. Spectrum of Research, 4(2). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/15

Copyright (c) 2024 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper presents a comprehensive framework for privacy-preserving AI analytics in cloud computing environments that enables cross-organizational data collaboration without compromising sensitive information. The proposed approach integrates federated learning with differential privacy and homomorphic encryption to address fundamental privacy challenges in multi-party analytics scenarios. The federated learning architecture maintains data locality while differential privacy mechanisms provide mathematical guarantees against inference attacks, and homomorphic encryption secures model aggregation processes. Experimental evaluation across diverse datasets demonstrates the framework achieves 83.18% model accuracy with privacy budget ε=0.5, representing only a 13.01% reduction compared to non-private baselines while providing robust privacy guarantees. Performance analysis reveals that the computational overhead of homomorphic encryption introduces a 684.8% training time increase for large models, highlighting critical efficiency trade-offs. The framework significantly outperforms previous approaches, improving data quality by up to 52.57% while reducing malicious participant exposure by approximately 16%. Extensive scalability testing confirms system viability across heterogeneous organizational environments with varying computational resources. This research establishes a foundation for privacy-compliant AI collaboration that balances utility preservation with stringent privacy requirements in regulated domains including healthcare, finance, and transportation.

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