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2024 | Book

Blockchain-Based Data Security in Heterogeneous Communications Networks

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About this book

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

Frontmatter
Chapter 1. Introduction
Abstract
Future communication network is envisioned to be highly heterogeneous with various stakeholders from different trust domains, including mobile operators, technological vendors, etc. As service requirements and user dynamics become more dynamic, artificial intelligence (AI)-assisted network management is a promising approach to increase service satisfaction and reduce operational costs. However, the AI-assisted approach puts stringent demand on data volume and quality, which requires new data security approaches across the trust boundaries of network stakeholders. In this chapter, we first review the features of future heterogeneous communication networks (HCN) to highlight the need of an advanced network architecture with data-driven network management methods. To manage data lifecycle events and preserve data security among heterogeneous network stakeholders, we introduce blockchain-based data management (DM) for HCN. We then present the design challenges in balancing privacy, efficiency, and fairness in DM with the decentralization nature of the blockchain and discuss three representative data security approaches: reliable data provenance, transparent data query, and fair data marketing. Finally, we provide the aim of the monograph and the overview the main contents of the following chapters.
Dongxiao Liu, Xuemin (Sherman) Shen
Chapter 2. Fundamental Data Security Technologies
Abstract
In this chapter, we introduce the fundamental technologies for achieving blockchain-based data security in HCN. First, we discuss basic crypto technologies from notations to hash functions. Second, we investigate basic blockchain technologies, with three exemplary blockchains, including Bitcoin, Ethereum, and Hyperledger Fabric. This will help us understand the working and design principles for blockchain-based applications. Third, we present state-of-the-art privacy-enhancing technologies for blockchain, including various constructions of the widely used zero-knowledge proof and anonymous credential techniques. Finally, we discuss computation models for blockchain and show how verifiable computations from cryptography or trusted hardware can boost blockchain efficiency and privacy.
Dongxiao Liu, Xuemin (Sherman) Shen
Chapter 3. Reliable Data Provenance in HCN
Abstract
Network provenance refers to collecting and storing network runtime data and events to construct a provenance graph, which can be used for root-cause analysis of network errors. As the future networks are embracing a distributed and heterogeneous architecture, reliable data provenance across network trust domains become a challenging issue. In this chapter, we investigate the blockchain-based data provenance approach in HCN. First, we review the motivations, applications, and requirements of designing reliable provenance architecture. Then, we discuss related works to highlight the design challenges in balancing decentralization and efficiency in network data provenance. To address the challenges, we present a representative blockchain-based distributed provenance scheme. More specifically, a multi-level query index based on a provenance graph is designed with succinct on-chain digests. By tailoring the designs of SNARK-based on/off-chain computation models, efficient cross-domain provenance query is achieved with correctness and integrity guarantees. Security analysis and experimental results demonstrate the efficiency of the proposed scheme.
Dongxiao Liu, Xuemin (Sherman) Shen
Chapter 4. Transparent Data Query in HCN
Abstract
Data query enables a data user to quickly find and retrieve data according to the user-specified keywords, range, or other query metrics. Data query plays a vital role in supporting many data-intensive applications in future networks. As data are generated and distributed at heterogeneous network entities, data query is often conducted by a third party that is out of the trust domain of the query user. In this chapter, we investigate transparent data query services for HCN. First, we identify the necessity for transparent VNF query and slice configuration across different network resource providers and propose a blockchain-based verifiable query scheme for NFV-enabled network management. Second, we integrate SNARG-based on/off-chain computation model to achieve succinct storage of a query dictionary and efficient verifications of query results. At the same time, we further investigate the random access memory (RAM) issue of SNARG-based solution that causes inefficient proving overheads. To address the efficiency challenge, we design a two-level SNARG system that conducts a key query to reduce search space for a full query. From pre-computed authenticators and a Merkle tree, we design a dictionary pruning scheme to efficiently generate an aggregated authenticator for the verifications in the SNARG system for the full query. We analyze the security properties of each component in the proposed scheme to achieve verifiable data query on the blockchain. We conduct extensive experiments with a consortium network to demonstrate the on/off-chain efficiency of the proposed scheme. With our proposed dictionary pruning strategy, off-chain proof generation overheads can be significantly reduced.
Dongxiao Liu, Xuemin (Sherman) Shen
Chapter 5. Fair Data Marketing in HCN
Abstract
In data marketing, a data owner can sell his/her data to a data buyer for profit considerations. The data buyer can utilize the data for various data-intensive applications, such as AI-assisted network management in HCN. With data privacy laws taking effect, it is essential to preserve the rights of data owners including data access control and identity privacy. At the same time, fair payment for the data owner and honest data delivery for the data buyer are also critical for the development of data marketing. In this chapter, we propose a blockchain–cloud data marketing scheme that complies with privacy regulations and preserves marketing fairness. First, we adopt a hybrid marketing model that utilizes the cloud server as a powerful data storage unit and the blockchain as a reliable controller of data marketing. By doing so, on-chain storage and computation costs are significantly reduced by only recording critical data marketing operations rather than the large volume of data. Second, we design succinct commitments of data marketing operations for data owners, data buyers, and the cloud server with efficient on-chain verifications. Through financial incentives and accountability enforcement, the proposed scheme achieves fair data marketing even with a rational off-chain cloud server. Third, we tailor the designs of multi-message PS signature and the threshold cryptograph for distributed management of data owners’ anonymous credentials. Specifically, distributed credential issuance and threshold identity tracing are realized without a single certificate authority. We conduct thorough security analysis and extensive experiments on a consortium blockchain network with different settings, which demonstrates that the proposed marketing scheme achieves the security goals and is practical for implementation.
Dongxiao Liu, Xuemin (Sherman) Shen
Chapter 6. Conclusion and Future Works
Abstract
In this chapter, we first conclude this monograph and briefly summarize the data security challenges in future networks. Three blockchain-based data security approaches: Reliable data provenance, transparent data query, and fair data marketing are discussed, which not only realize a decentralized solution but address the efficiency, privacy, and fairness challenges with a blockchain architecture. Then, we investigate potential research directions, including on/off-chain computation models with modular designs, and multi-party fair AI model sharing with efficient verifications.
Dongxiao Liu, Xuemin (Sherman) Shen
Backmatter
Metadata
Title
Blockchain-Based Data Security in Heterogeneous Communications Networks
Authors
Dongxiao Liu
Xuemin (Sherman) Shen
Copyright Year
2024
Electronic ISBN
978-3-031-52477-6
Print ISBN
978-3-031-52476-9
DOI
https://doi.org/10.1007/978-3-031-52477-6