Blockchain-Based Data Security in Heterogeneous Communications Networks

✍ Scribed by Dongxiao Liu, Xuemin (Sherman) Shen

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 200
• Category: Library

✦ Synopsis

This book investigates data security approaches in Heterogeneous Communications Networks (HCN). First, the book discusses the urgent need for a decentralized data management architecture in HCN. The book investigates preliminaries and related research to help readers obtain a comprehensive picture of the research topic. Second, the book presents three blockchain-based approaches for data management in HCN: data provenance, data query, and data marketing. Finally, based on the insights and experiences from the presented approaches, the book discusses future research directions.

✦ Table of Contents

Preface
Contents
Acronyms
1 Introduction
1.1 Heterogeneous Communications Networks (HCN)
1.2 Emerging Architecture of Mobile Network
1.3 AI-Assisted Network Management in HCN
1.3.1 Challenges of Network Management
1.3.2 Artificial Intelligence for Networking
1.4 Data Management in HCN
1.4.1 Data Lifecycle in HCN
1.4.2 Centralized Data Management Approach
1.4.3 Blockchain-Based Data Management Approach
1.4.4 Balancing Efficiency, Privacy, and Fairness in Blockchain-Based DM
1.5 Blockchain-Based Data Security Approaches
1.5.1 Reliable Data Provenance
1.5.1.1 Use Case
1.5.1.2 Design Challenges
1.5.2 Transparent Data Query
1.5.2.1 Use Case
1.5.2.2 Design Challenges
1.5.3 Fair Data Marketing
1.5.3.1 Use Case
1.5.3.2 Design Challenges
1.6 Aim of the Monograph
References
2 Fundamental Data Security Technologies
2.1 Basic Crypto Technologies
2.1.1 Notations
2.1.2 Digital Signature
2.1.3 Data Encryption
2.1.3.1 Symmetric Key-Based Encryption
2.1.3.2 Public Key-Based Encryption
2.1.4 Hash Function
2.2 Basic Blockchain Technologies
2.2.1 Data Structures
2.2.2 Identity and Transaction Management
2.2.3 Consensus Protocol and Reward Mechanism
2.2.4 Smart Contract
2.2.5 Channel in Hyperledger Fabric
2.2.6 Performance Metrics
2.2.7 Testing Network
2.3 Privacy-Enhancing Technologies for Blockchain
2.3.1 Cryptographic Commitment
2.3.1.1 Pedersen Commitment
2.3.1.2 Polynomial Commitment
2.3.1.3 Vector Commitment
2.3.1.4 Merkle Tree
2.3.2 Zero-Knowledge Proof
2.3.2.1 Sigma Protocol
2.3.2.2 Fiat-Shamir Heuristic
2.3.2.3 ZKP for Algebraic Relations
2.3.3 zk-SNARK
2.3.3.1 Workflow of zk-SNARK
2.3.3.2 Quadratic Arithmetic Program (QAP)
2.3.3.3 Non-Universal zk-SNARK
2.3.3.4 Universal zk-SNARK
2.3.3.5 Open-Source Implementations
2.3.4 Commit-and-Prove ZKP
2.3.4.1 Application Scenario
2.3.4.2 Constructions
2.3.5 Anonymous Credential
2.3.5.1 Definitions
2.3.5.2 Representative Constructions
2.4 On/off-chain Computation Model for Blockchain
2.4.1 SNARK-Based Approach
2.4.2 Trusted Execution Environment-Based Approach
2.4.2.1 Useful Mechanisms
2.4.2.2 Integrating Blockchain with SGX
2.4.2.3 Implementations
2.5 Summary
References
3 Reliable Data Provenance in HCN
3.1 Motivations and Applications
3.2 Application Requirements
3.2.1 Provenance Trustworthiness
3.2.2 Provenance Privacy
3.2.3 Provenance Query
3.3 State-of-the-Art Data Provenance Approaches
3.3.1 Non-Blockchain-Based Approach
3.3.2 Blockchain-Based Approach
3.3.3 Decentralization and Efficiency Dilemma
3.4 Use Case: Distributed Network Provenance
3.4.1 Network Provenance Model
3.4.1.1 Graph-Based Network Provenance
3.4.1.2 Distributed Network Provenance Model
3.4.2 Defining Archiving Security
3.4.2.1 Security Model
3.4.2.2 Design Goals
3.4.3 Building Blocks
3.4.3.1 Cryptographic Primitives
3.4.3.2 Pinocchio-Based VC
3.4.4 Representative Constructions
3.4.4.1 System Setup by TA
3.4.4.2 On-chain Digest Construction by Administrators
3.4.4.3 Cross-Domain Provenance Query
3.4.4.4 Verification of Provenance Query
3.4.5 Security Analysis
3.4.5.1 Security Assumptions
3.4.5.2 Blockchain Security
3.4.5.3 VC Security
3.4.5.4 Security of Merkle Proof
3.4.5.5 Archiving Security
3.4.6 Performance Evaluation
3.4.6.1 Digest Performance Analysis
3.4.6.2 Off-chain Performance
3.4.6.3 On-chain Performance Analysis
3.4.6.4 Multi-Level Query Strategy
3.5 Summary and Discussions
References
4 Transparent Data Query in HCN
4.1 Motivations and Applications
4.2 Application Requirements
4.2.1 Privacy
4.2.2 Trustworthiness
4.2.3 Efficiency
4.3 State-of-the-Art Data Query Approaches
4.3.1 Cloud-Based Data Query
4.3.2 Blockchain-Based Data Query
4.3.3 Decentralization and Efficiency Dilemma
4.4 Use Case: Blockchain-Based VNF Query
4.4.1 VNF Query in HCN
4.4.2 Threat Model and Design Goals
4.4.3 Building Blocks
4.4.3.1 Cryptographic Notations
4.4.3.2 Commitment Schemes
4.4.3.3 SNARG
4.4.4 Representative Constructions
4.4.4.1 System Setup
4.4.4.2 Design of Pruning Function
4.4.4.3 VNF Listing
4.4.4.4 VNF Query Construction
4.4.4.5 VNF Query Processing
4.4.4.6 VNF Query Verification
4.4.5 Security Analysis
4.4.5.1 Security of SNARG
4.4.5.2 Security of Commitments
4.4.5.3 Dictionary Pruning Security
4.4.5.4 Verifiable VNF Query
4.4.6 Performance Evaluation
4.4.6.1 Implementation Overview
4.4.6.2 Off-Chain Benchmarks
4.4.6.3 Performance Gain by Dictionary Pruning
4.4.6.4 Overheads for Dictionary Pruning
4.4.6.5 On-Chain Benchmarks
4.5 Summary and Discussions
References
5 Fair Data Marketing in HCN
5.1 Motivations and Applications
5.2 Application Requirements
5.2.1 Regulation Compliance
5.2.2 Identity Privacy
5.2.3 Data Marketing Fairness
5.3 State-of-the-Art Data Marketing Approaches
5.3.1 Centralized Data Marketing
5.3.2 Decentralized Data Marketing
5.3.2.1 On-Chain Model
5.3.2.2 On/off-Chain Model
5.3.3 Decentralization and Fairness Dilemma
5.4 Use Case: Blockchain–Cloud Fair Data Marketing
5.4.1 Blockchain–Cloud Data Marketing Model
5.4.2 Security Model and Goals
5.4.3 Design Goals
5.4.4 Building Blocks
5.4.4.1 Cryptographic Notations
5.4.4.2 ElGamal Encryption
5.4.4.3 Zero-Knowledge Proof
5.4.4.4 Multi-message PS Signature
5.4.4.5 Public Verifiable Secret Sharing (PVSS)
5.4.5 Representative Constructions
5.4.5.1 Setup
5.4.5.2 Registration
5.4.5.3 Data Listing
5.4.5.4 Data Trading
5.4.5.5 Tracing
5.4.6 Security Analysis
5.4.6.1 Blockchain Security
5.4.6.2 Credential Security
5.4.6.3 Consortium Management
5.4.6.4 Marketing Fairness
5.4.7 Performance Evaluation
5.4.7.1 Complexity Analysis
5.4.7.2 Experimental Setup
5.4.7.3 Off-Chain Performance
5.4.7.4 On-Chain Performance
5.5 Summary and Discussions
References
6 Conclusion and Future Works
6.1 Conclusion
6.1.1 Reliable Data Provenance
6.1.2 Transparent Data Query
6.1.3 Fair Data Marketing
6.2 Future Works
6.2.1 On/off-Chain Computation Model with Modular Designs
6.2.2 Multi-party Fair AI Model Sharing
Index