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Schnorr Approval-Based Secure and Privacy-Preserving IoV Data Aggregation

Rui Liu, Jianping Pan

TL;DR

SADA introduces a two-layer IoV data aggregation framework that enables privacy-preserving data collection without heavy vehicle-side cryptography. It combines recoverable masking with a Schnorr-based data approval mechanism (MuSig-inspired) to ensure authenticity of aggregated data while preserving vehicle identities and trajectories behind cluster heads. The framework includes a recoverable masking protocol, a data-approval generation process, pre-checking, cluster-key auditing, and a rigorous ROM-based security analysis, achieving low computation and communication overhead for vehicles. The approach eliminates the need for pseudonym management and provides clear liability separation, making it practical for real-world IoV deployments while defending against fake data injection and input invalidation attacks.

Abstract

Secure and privacy-preserving data aggregation in the Internet of Vehicles (IoV) continues to be a focal point of interest in both the industry and academia. Aiming at tackling the challenges and solving the remaining limitations of existing works, this paper introduces a novel Schnorr approval-based IoV data aggregation framework based on a two-layered architecture. In this framework, a server can aggregate the IoV data from clusters without inferring the raw data, real identity and trajectories of vehicles. Notably, we avoid incorporating the widely-accepted techniques such as homomorphic encryption and digital pseudonym to avoid introducing high computation cost to vehicles. We propose a novel concept, data approval, based on the Schnorr signature scheme. With the approval, the fake data injection attack carried out by a cluster head can be defended against. The separation of liability is achieved as well. The evaluation shows that the framework is secure and lightweight for vehicles in terms of the computation and communication costs.

Schnorr Approval-Based Secure and Privacy-Preserving IoV Data Aggregation

TL;DR

SADA introduces a two-layer IoV data aggregation framework that enables privacy-preserving data collection without heavy vehicle-side cryptography. It combines recoverable masking with a Schnorr-based data approval mechanism (MuSig-inspired) to ensure authenticity of aggregated data while preserving vehicle identities and trajectories behind cluster heads. The framework includes a recoverable masking protocol, a data-approval generation process, pre-checking, cluster-key auditing, and a rigorous ROM-based security analysis, achieving low computation and communication overhead for vehicles. The approach eliminates the need for pseudonym management and provides clear liability separation, making it practical for real-world IoV deployments while defending against fake data injection and input invalidation attacks.

Abstract

Secure and privacy-preserving data aggregation in the Internet of Vehicles (IoV) continues to be a focal point of interest in both the industry and academia. Aiming at tackling the challenges and solving the remaining limitations of existing works, this paper introduces a novel Schnorr approval-based IoV data aggregation framework based on a two-layered architecture. In this framework, a server can aggregate the IoV data from clusters without inferring the raw data, real identity and trajectories of vehicles. Notably, we avoid incorporating the widely-accepted techniques such as homomorphic encryption and digital pseudonym to avoid introducing high computation cost to vehicles. We propose a novel concept, data approval, based on the Schnorr signature scheme. With the approval, the fake data injection attack carried out by a cluster head can be defended against. The separation of liability is achieved as well. The evaluation shows that the framework is secure and lightweight for vehicles in terms of the computation and communication costs.
Paper Structure (25 sections, 2 theorems, 24 equations, 6 figures, 7 tables, 3 algorithms)

This paper contains 25 sections, 2 theorems, 24 equations, 6 figures, 7 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Masking Technique
  5. Schnorr Signatures
  6. Multi-Signature
  7. System and Threat Model
  8. System Model
  9. Threat Model
  10. Framework Overview
  11. Framework Design
  12. Recoverable Masking Protocol
  13. Data Approval Generation
  14. Data Approval Pre-Checking
  15. Regional Data Uploading
  16. ...and 10 more sections

Key Result

Lemma 1

Fix $N_\textup{v}$, $p_\textup{dh}$, $p_\textup{mk}$ where $p_\textup{dh}>p_\textup{mk}$, and $\{\mathit{data}_i\mid i\in N_\textup{v}\}$ where $\forall i \in N_\textup{v}$, $\mathit{data}_i \in \mathbb{Z}_{p_\textup{mk}}$. Then, where $\equiv$ denotes the identical distribution.

Figures (6)

  • Figure 1: Framework model.
  • Figure 2: Protocol of SADA.
  • Figure 3: An example of the maintained trees with $n_{\text{v}}=10$.
  • Figure 4: Comparison of the bad sub-approval identification.
  • Figure : Recoverable Masking Algorithm
  • ...and 1 more figures

Theorems & Definitions (3)

  • Lemma 1
  • Theorem 1
  • proof