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Systematic Survey on Privacy-Preserving Architectures for IoT and Vehicular Data Sharing: Techniques, Challenges, and Future Directions

Phat T. Tran-Truong, Vinh X. Q. Nguyen, Ha X. Son, Phien Nguyen-Ngoc, Khanh H. Vo, Triet M. Nguyen

TL;DR

This survey systematically analyzes 75 technical papers through a novel three-dimensional taxonomy classifying architectures into Decentralized Computation, Cryptography-based, and Distributed Ledger approaches, identifying emerging hybrid architectures combining complementary paradigms as the essential path forward.

Abstract

The proliferation of IoT and V2X systems generates unprecedented sensitive data at the network edge, demanding privacy-preserving architectures that enable secure sharing without exposing raw information. Contemporary solutions face a fundamental privacy-efficiency-trust trilemma: achieving strong privacy guarantees, computational efficiency for resource-constrained devices, and decentralized trust simultaneously remains intractable with single-paradigm approaches. This survey systematically analyzes 75 technical papers (2007--2025) through a novel three-dimensional taxonomy classifying architectures into Decentralized Computation, Cryptography-based, and Distributed Ledger approaches. Temporal analysis reveals dramatic acceleration during 2024--2025, with 48% of all papers published in this period -- Decentralized Computation dominates at 44% of contributions and 59% of 2025 publications. Comprehensive Security Threat Mapping and Technology Maturity Assessment demonstrate that mature solutions occupy narrow design regions excelling in one or two dimensions while compromising others, conclusively validating the trilemma hypothesis. We identify emerging hybrid architectures combining complementary paradigms as the essential path forward. Critical challenges including security guarantee composition across layers, multi-layer coordination overhead minimization, and post-quantum security integration must be addressed for practical deployment in next-generation intelligent transportation systems and IoT ecosystems.

Systematic Survey on Privacy-Preserving Architectures for IoT and Vehicular Data Sharing: Techniques, Challenges, and Future Directions

TL;DR

This survey systematically analyzes 75 technical papers through a novel three-dimensional taxonomy classifying architectures into Decentralized Computation, Cryptography-based, and Distributed Ledger approaches, identifying emerging hybrid architectures combining complementary paradigms as the essential path forward.

Abstract

The proliferation of IoT and V2X systems generates unprecedented sensitive data at the network edge, demanding privacy-preserving architectures that enable secure sharing without exposing raw information. Contemporary solutions face a fundamental privacy-efficiency-trust trilemma: achieving strong privacy guarantees, computational efficiency for resource-constrained devices, and decentralized trust simultaneously remains intractable with single-paradigm approaches. This survey systematically analyzes 75 technical papers (2007--2025) through a novel three-dimensional taxonomy classifying architectures into Decentralized Computation, Cryptography-based, and Distributed Ledger approaches. Temporal analysis reveals dramatic acceleration during 2024--2025, with 48% of all papers published in this period -- Decentralized Computation dominates at 44% of contributions and 59% of 2025 publications. Comprehensive Security Threat Mapping and Technology Maturity Assessment demonstrate that mature solutions occupy narrow design regions excelling in one or two dimensions while compromising others, conclusively validating the trilemma hypothesis. We identify emerging hybrid architectures combining complementary paradigms as the essential path forward. Critical challenges including security guarantee composition across layers, multi-layer coordination overhead minimization, and post-quantum security integration must be addressed for practical deployment in next-generation intelligent transportation systems and IoT ecosystems.
Paper Structure (63 sections, 1 equation, 5 figures, 7 tables)

This paper contains 63 sections, 1 equation, 5 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Cryptography-Focused Surveys
  4. Federated Learning and Distributed Machine Learning Surveys
  5. Trusted Execution Environment Surveys
  6. Blockchain and Distributed Ledger Surveys
  7. Hybrid and Cross-Architectural Surveys
  8. Positioning This Survey
  9. Methodology
  10. Search Strategy and Screening Process
  11. Taxonomy Development and Statistical Analysis
  12. Taxonomy Rationale and Classification Logic
  13. Distribution Analysis: Architectural Paradigm Preferences
  14. Temporal Trend Analysis: Evolution of Research Focus
  15. Technical Preliminaries
  16. ...and 48 more sections

Figures (5)

  • Figure 1: PRISMA Flow Diagram illustrating the systematic literature selection process. The methodology follows PRISMA guidelines, searching databases including Google Scholar, IEEE Xplore, and Scopus (2007--2025).
  • Figure 2: Taxonomy distribution of the 75 technical papers (2007--2025).
  • Figure 3: Temporal trend of research activity by architectural paradigm (2021--2025).
  • Figure 4: Proposed generic architecture for privacy-preserving data sharing. It illustrates the data flow from IoT/Vehicular owners through privacy primitives to decentralized storage and authorized consumers.
  • Figure 5: Proposed Taxonomy of Privacy-Preserving Architectures: Three architectural paradigms (Decentralized Computation, Cryptography, Distributed Ledger).