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FL-DECO-BC: A Privacy-Preserving, Provably Secure, and Provenance-Preserving Federated Learning Framework with Decentralized Oracles on Blockchain for VANETs

Sathwik Narkedimilli, Rayachoti Arun Kumar, N. V. Saran Kumar, Ramapathruni Praneeth Reddy, Pavan Kumar C

TL;DR

This paper tackles privacy, security, and data provenance in VANET-based Federated Learning by proposing FL-DECO-BC, a framework that uses decentralized oracles on a blockchain to securely access external data and securely aggregate model updates. It combines local training on vehicles, encrypted weight sharing through DECO, verifiable SMPC-based aggregation, and on-chain storage with immutable provenance records to ensure trust and accountability. Key contributions include a provenance-preserving design, cryptographic guarantees through ZKPs and SMPC, and BAN-logic–driven validation of secure communication steps. The approach aims to enable privacy-conscious, trustworthy, and scalable FL for advanced traffic management and safety applications in VANETs.

Abstract

Vehicular Ad-Hoc Networks (VANETs) hold immense potential for improving traffic safety and efficiency. However, traditional centralized approaches for machine learning in VANETs raise concerns about data privacy and security. Federated Learning (FL) offers a solution that enables collaborative model training without sharing raw data. This paper proposes FL-DECO-BC as a novel privacy-preserving, provably secure, and provenance-preserving federated learning framework specifically designed for VANETs. FL-DECO-BC leverages decentralized oracles on blockchain to securely access external data sources while ensuring data privacy through advanced techniques. The framework guarantees provable security through cryptographic primitives and formal verification methods. Furthermore, FL-DECO-BC incorporates a provenance-preserving design to track data origin and history, fostering trust and accountability. This combination of features empowers VANETs with secure and privacy-conscious machine-learning capabilities, paving the way for advanced traffic management and safety applications.

FL-DECO-BC: A Privacy-Preserving, Provably Secure, and Provenance-Preserving Federated Learning Framework with Decentralized Oracles on Blockchain for VANETs

TL;DR

This paper tackles privacy, security, and data provenance in VANET-based Federated Learning by proposing FL-DECO-BC, a framework that uses decentralized oracles on a blockchain to securely access external data and securely aggregate model updates. It combines local training on vehicles, encrypted weight sharing through DECO, verifiable SMPC-based aggregation, and on-chain storage with immutable provenance records to ensure trust and accountability. Key contributions include a provenance-preserving design, cryptographic guarantees through ZKPs and SMPC, and BAN-logic–driven validation of secure communication steps. The approach aims to enable privacy-conscious, trustworthy, and scalable FL for advanced traffic management and safety applications in VANETs.

Abstract

Vehicular Ad-Hoc Networks (VANETs) hold immense potential for improving traffic safety and efficiency. However, traditional centralized approaches for machine learning in VANETs raise concerns about data privacy and security. Federated Learning (FL) offers a solution that enables collaborative model training without sharing raw data. This paper proposes FL-DECO-BC as a novel privacy-preserving, provably secure, and provenance-preserving federated learning framework specifically designed for VANETs. FL-DECO-BC leverages decentralized oracles on blockchain to securely access external data sources while ensuring data privacy through advanced techniques. The framework guarantees provable security through cryptographic primitives and formal verification methods. Furthermore, FL-DECO-BC incorporates a provenance-preserving design to track data origin and history, fostering trust and accountability. This combination of features empowers VANETs with secure and privacy-conscious machine-learning capabilities, paving the way for advanced traffic management and safety applications.
Paper Structure (18 sections, 2 equations, 1 figure, 1 table)

This paper contains 18 sections, 2 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Vehicular Ad-Hoc Networks (VANETs)
  4. Decentralized Oracles (DECO)
  5. Federated Learning(FL)
  6. Blockchain
  7. Cryptographic Primitives
  8. Literature Review
  9. Proposed Model
  10. The FL-DECO-BC Workflow
  11. Assumptions for the FL-DECO-BC Framework
  12. BAN Logic Analysis for FL-DECO-BC Framework
  13. Analysis of Proposed Framework
  14. Anonymity
  15. Non-traceability and Impersonation attacks
  16. ...and 3 more sections

Figures (1)

  • Figure 1: Flow Diagram of FL-DECO-BC Framework