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Enhancing Privacy in Distributed Systems with Laplace Quantization Mechanism

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Kalidindi Pavan Teja Satya Varma
G. Balasaisrujankumarrao
Nagesh Bhattu Sristy
Published in
Security and Privacy
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the integration of privacy protection in next-generation wireless networks and distributed systems, focusing on the challenges of communication efficiency and privacy concerns in distributed learning. It introduces the Laplace quantization mechanism as a solution to achieve both communication efficiency and privacy protection, addressing the limitations of existing techniques. The chapter validates the proposed approach on MNIST and CIFAR-10 datasets, demonstrating its efficacy in enhancing privacy and maintaining high accuracy. Additionally, it compares the performance of Laplace-RQ-SGD with Gaussian-LRQ-SGD, highlighting the advantages of the Laplace quantizer in terms of accuracy, loss, and communication efficiency. The chapter concludes by emphasizing the strong balance between communication efficiency, privacy, and accuracy achieved by the Laplace-RQ-SGD method in distributed learning frameworks.
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previous chapter Smart Detection of Indian Counterfeit Currency Notes using Deep Learning Techniques
next chapter A Lightweight Intrusion Detection Framework for IoT Using Fisher Score Feature Filtering and ML Models
Title
Enhancing Privacy in Distributed Systems with Laplace Quantization Mechanism
Authors
Kalidindi Pavan Teja Satya Varma
G. Balasaisrujankumarrao
Nagesh Bhattu Sristy
Copyright Year
2026
Book
Security and Privacy

Print ISBN: 978-3-032-12833-1

Electronic ISBN: 978-3-032-12834-8

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-12834-8_10
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