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FAPL-DM-BC: A Secure and Scalable FL Framework with Adaptive Privacy and Dynamic Masking, Blockchain, and XAI for the IoVs

Sathwik Narkedimilli, Amballa Venkata Sriram, Sujith Makam, MSVPJ Sathvik, Sai Prashanth Mallellu

Abstract

The FAPL-DM-BC solution is a new FL-based privacy, security, and scalability solution for the Internet of Vehicles (IoV). It leverages Federated Adaptive Privacy-Aware Learning (FAPL) and Dynamic Masking (DM) to learn and adaptively change privacy policies in response to changing data sensitivity and state in real-time, for the optimal privacy-utility tradeoff. Secure Logging and Verification, Blockchain-based provenance and decentralized validation, and Cloud Microservices Secure Aggregation using FedAvg (Federated Averaging) and Secure Multi-Party Computation (SMPC). Two-model feedback, driven by Model-Agnostic Explainable AI (XAI), certifies local predictions and explanations to drive it to the next level of efficiency. Combining local feedback with world knowledge through a weighted mean computation, FAPL-DM-BC assures federated learning that is secure, scalable, and interpretable. Self-driving cars, traffic management, and forecasting, vehicular network cybersecurity in real-time, and smart cities are a few possible applications of this integrated, privacy-safe, and high-performance IoV platform.

FAPL-DM-BC: A Secure and Scalable FL Framework with Adaptive Privacy and Dynamic Masking, Blockchain, and XAI for the IoVs

Abstract

The FAPL-DM-BC solution is a new FL-based privacy, security, and scalability solution for the Internet of Vehicles (IoV). It leverages Federated Adaptive Privacy-Aware Learning (FAPL) and Dynamic Masking (DM) to learn and adaptively change privacy policies in response to changing data sensitivity and state in real-time, for the optimal privacy-utility tradeoff. Secure Logging and Verification, Blockchain-based provenance and decentralized validation, and Cloud Microservices Secure Aggregation using FedAvg (Federated Averaging) and Secure Multi-Party Computation (SMPC). Two-model feedback, driven by Model-Agnostic Explainable AI (XAI), certifies local predictions and explanations to drive it to the next level of efficiency. Combining local feedback with world knowledge through a weighted mean computation, FAPL-DM-BC assures federated learning that is secure, scalable, and interpretable. Self-driving cars, traffic management, and forecasting, vehicular network cybersecurity in real-time, and smart cities are a few possible applications of this integrated, privacy-safe, and high-performance IoV platform.
Paper Structure (19 sections, 1 figure, 1 table)

This paper contains 19 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Federated Adaptive Privacy-Aware Learning (FAPL)
  4. Dynamic Masking (DM)
  5. Secure Multi-Party Computation (SMPC)
  6. Blockchain
  7. Explainable AI (XAI)
  8. Literature Review
  9. Proposed Framework
  10. The FAPL-DM-BC Workflow
  11. Assumptions for the FAPL-DM-BC Framework
  12. BAN Logic Analysis for FAPL-DM-BC Framework
  13. Analysis of Proposed Framework
  14. Anonymity
  15. Non-traceability and Impersonation attacks
  16. ...and 4 more sections

Figures (1)

  • Figure 1: The proposed FAPL-DM-BC Framework