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A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions

Natarajan Deepa, Quoc-Viet Pham, Dinh C. Nguyen, Sweta Bhattacharya, B Prabadevi, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Fang Fang, Pubudu N. Pathirana

arXiv:2009.00858v2cs.CRcs.DC

TL;DR

Big data faces security, privacy, management, and analytics challenges, motivating investigation of blockchain integration. The paper comprehensively surveys blockchain services, applications, projects, challenges, and future directions, concluding that blockchain can help manage and secure accumulated data while supporting analytics.

  • Problem

    Big data presents unresolved challenges in analytics, management, privacy, security, collection, storage, sharing, and data quality.

  • Method

    The paper conducts a comprehensive state-of-the-art survey of blockchain services, vertical applications, representative projects, challenges, and future directions for big data.

  • Results

    The survey concludes that blockchain can effectively manage and secure substantial big-data accumulations, while big-data analytics provides insights for predictions.

  • Takeaways & Limitations

    Blockchain’s decentralized and immutable ledger is presented as supporting data integrity and big-data analytics across surveyed services and applications.

Abstract

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Big data has generated strong interest in various scientific and engineering domains over the last few years. Despite many advantages and applications, there are many challenges in big data to be tackled for better quality of service, e.g., big data analytics, big data management, and big data privacy and security. Blockchain with its decentralization and security nature has the great potential to improve big data services and applications. In this article, we provide a comprehensive survey on blockchain for big data, focusing on up-to-date approaches, opportunities, and future directions. First, we present a brief overview of blockchain and big data as well as the motivation behind their integration. Next, we survey various blockchain services for big data, including blockchain for secure big data acquisition, data storage, data analytics, and data privacy preservation. Then, we review the state-of-the-art studies on the use of blockchain for big data applications in different vertical domains such as smart city, smart healthcare, smart transportation, and smart grid. For a better understanding, some representative blockchain-big data projects are also presented and analyzed. Finally, challenges and future directions are discussed to further drive research in this promising area.

I. INTRODUCTION

Big data offers broad analytical and industrial value but raises persistent security, privacy, management, and quality challenges. The survey addresses a stated gap by comprehensively reviewing blockchain approaches, applications, opportunities, and future directions for big data.

  • I. INTRODUCTION: Big data is attracting broad scientific and engineering interest despite unresolved security, privacy, management, and analytics challenges.Sensitive personal information makes security and privacy particularly important.
  • A. State of the Arts and Our Contributions: Existing surveys do not comprehensively and up-to-date address blockchain applicability across big data applications.Earlier work is described as short, outdated, or limited to related topics.
  • A. State of the Arts and Our Contributions: The survey reviews blockchain’s potential to support big data analytics through dirty-data control, improved security and privacy, data quality, and data-sharing management.These are presented as motivations and potential benefits rather than evaluated outcomes.
  • A. State of the Arts and Our Contributions: It surveys four blockchain services for big data: secure acquisition, secure storage, data analytics, and privacy preservation.The service categories define the survey’s main technical coverage.
  • A. State of the Arts and Our Contributions: It also reviews blockchain-big data applications across smart healthcare, transportation and logistics, smart grid, and smart cities, alongside representative projects.The paper analyzes both vertical applications and selected projects.
  • A. State of the Arts and Our Contributions: The survey identifies research challenges and highlights open opportunities intended to provide a roadmap for future research.The roadmap follows from the state-of-the-art survey.

B. The Survey Organization

The article is organized from foundational blockchain and big-data background through blockchain services, applications, projects, challenges, and conclusions. Its blockchain overview emphasizes ledger-based, decentralized, cryptographically secured transaction management.

  • B. The Survey Organization: Section II introduces blockchain and big data, then discusses motivations for integrating the technologies.This foundational material precedes the survey’s main thematic sections.
  • B. The Survey Organization: Sections III and IV cover blockchain services for big data and blockchain-big data applications and projects, respectively.The services section addresses acquisition, storage, analytics, and privacy preservation elsewhere in the paper’s organization.
  • B. The Survey Organization: Section V discusses research challenges, issues, and future directions, while Section VI concludes the article.The organization reserves the final sections for limitations and forward-looking discussion.
  • A. Blockchain: Blockchain is presented as a list of encrypted records called blocks maintained in a decentralized peer-to-peer network.Participants can monitor transactions, while cryptoeconomics combines software engineering, cryptography, and distributed computing.
  • A. Blockchain: Blockchain platforms are classified as public, private, or hybrid according to their application context and participation controls.Public systems are broadly visible and decentralized, whereas private systems are permissioned.
  • A. Blockchain: Immutability, decentralization, and security are identified as core blockchain properties supported by replicated ledgers, consensus, and cryptography.These properties distinguish blockchain from systems governed by a single authority.

B. Big Data

Big data is characterized by large-scale, fast, heterogeneous, and potentially unreliable information that creates storage, processing, sharing, security, and quality challenges. Blockchain is discussed as a potential way to structure, protect, and coordinate access to such data.

  • B. Big Data: Big data is commonly characterized by volume, velocity, variety, and veracity.These four dimensions capture data scale, generation speed, heterogeneity, and trustworthiness.
  • B. Big Data: Variety creates locality, heterogeneity, and dirty or noisy data challenges across distributed sources and formats.Data may be distributed across physical locations and differ in types, models, formats, and semantics.
  • B. Big Data: Big data growth supports analytics for organizational insight while intensifying challenges involving security, privacy, reliability, sharing, and data quality.The paper connects these challenges to the expanding use of advanced analytical tools.
  • B. Big Data: Blockchain is proposed as a way to improve big-data security and privacy when data are stored at third-party locations such as cloud services.The motivation is that organizations may lack control over externally stored data.
  • B. Big Data: Blockchain’s immutability is presented as a means to protect data integrity against record tampering that could influence analytics.The paper states that modifying blockchain data would require changing records across at least 50% of nodes.
  • B. Big Data: Blockchain-integrated big data can support real-time fraud monitoring, transaction assessment, and analytics-based financial decisions.The paper describes real-time transaction monitoring and near-real-time cross-border settlement as examples.
  • B. Big Data: Blockchain can facilitate trusted data access and sharing while recording experiments and structuring data for improved quality and prediction work.Authorized users can access shared data with fewer checks, and recorded experiments can reduce repeated analysis.

III. BLOCKCHAIN SERVICES FOR BIG DATA

Big data services face security risks across collection, sharing, storage, and analysis, especially when cloud and third-party systems are involved. The survey therefore examines blockchain-based services for acquisition, storage, analytics, and privacy preservation.

  • III. BLOCKCHAIN SERVICES FOR BIG DATA: Big data services face security threats from third-party applications and intruders across collection, sharing, storage, and analysis.Cloud computing is widely used despite concerns about data theft and server disruption.
  • III. BLOCKCHAIN SERVICES FOR BIG DATA: The survey organizes blockchain-based big data services into acquisition, storage, analytics, and privacy-preservation approaches.This taxonomy structures the section’s review of blockchain applications to the big-data lifecycle.
  • III. BLOCKCHAIN SERVICES FOR BIG DATA: Blockchain can structure data from diversified, differently formatted sources so application-specific predictions can be made.Consensus algorithms are described as supporting data integrity during this structuring process.
  • 1) Blockchain for Secure Big Data Collection:: Secure data collection is necessary because suspicious sources and communication links expose acquired big data to malicious attacks and threats.The paper identifies data collection as an important stage in data processing.
  • 1) Blockchain for Secure Big Data Collection:: Blockchain-based secure collection schemes are reviewed for mobile crowdsensing frameworks involving cloud servers and mobile terminals.The cited mobile crowdsensing setting targets industrial IoT environments.

2) Blockchain for Secure Big Data Transmission/Sharing:

Blockchain-based transmission and sharing mechanisms aim to secure big-data exchange while reducing processing, storage, and network overhead. The surveyed approaches use distributed validation, caching, smart contracts, and cryptographic protection across transmission and storage workflows.

  • Blockchain records shared big data with signatures and hash values to support secure transmission from data sources to analytics.The surveyed model uses blockchain’s decentralized and immutable properties for reliable data sharing.
  • A blockchain edge model reduces computation through proof-of-collaboration and accesses cached data to reduce response time and storage overhead.Express transactions and hollow blocks further target network efficiency and asynchronous transaction validation.
  • Blockchain integrated with IPFS addresses cloud and medical-data security concerns by using decentralized file storage and unique file hashes.The passage describes access control needs for sensitive medical records and IPFS’s role in reducing file redundancy.
  • Blockchain-based storage approaches use smart contracts, hash information, cryptographic algorithms, and digital signatures to control sharing and protect records.The examples include educational records linked to blockchain-stored hash information and encrypted through cryptographic mechanisms.

3) Blockchain for Big Data Storage Infrastructure:

The surveyed storage infrastructure combines blockchain with cloud, edge, and AI-oriented data systems to address growing storage, processing, transmission, and trust requirements. These approaches emphasize decentralized recording and secure repositories for data-driven analysis.

  • Big-data growth creates storage, processing, and transmission challenges that cloud computing addresses through shared networking, storage, and computing resources.
  • Edge and cloud computing support large-scale data analysis using machine-learning and deep-learning methods across heterogeneous data sources.The example concerns vehicular social networks whose data come from social-network companies, vehicle manufacturers, and vehicle-management agencies.
  • Centralized AI training can allow data tampering, motivating decentralized AI through blockchain integration.Blockchain provides a distributed ledger in which data can be recorded and transacted.
  • Blockchain supplies secure, reliable, and trusted data repositories for AI techniques that depend on data to learn and make decisions.
  • Privacy-preserving data management is increasingly important because organizations gather, analyze, and manage large volumes of personal information.

2) Blockchain for Privacy Preservation in Big Data Storage:

Blockchain-based privacy-preservation approaches respond to risks from third-party data management, smart-city infrastructures, and healthcare information systems. The surveyed mechanisms combine access control, encryption, distributed storage, and auditable records.

  • Third-party collection and management of personal information expose users to security breaches and data misuse, motivating blockchain-based privacy solutions.
  • Smart-city infrastructure requires secure and reliable handling of IoT and big data despite maintenance, adaptability, and cost challenges.
  • Blockchain-based auditing can trace complete file histories and support batch verification of auditing proofs for security and privacy.
  • Healthcare IoT generates sensitive data for diagnosis, prediction, and treatment while introducing security and privacy risks during transfer and logging.
  • Lightweight cryptography integrated with blockchain provides patient medical-record access control with improved privacy and security.The cited framework uses public and private keys alongside lightweight cryptographic techniques.
  • Smart contracts record healthcare events, monitor patients in real time, notify professionals when intervention is needed, and authenticate untampered electronic health records.

C. Blockchain Big Data in Smart Transportation

Blockchain is surveyed as a component of intelligent transportation and related energy systems, supporting secure data handling, autonomous infrastructure, and decentralized interactions. Examples include layered ITS architectures, offline encrypted storage, and distributed key management.

  • Blockchain, IoT, AI, deep learning, and Mobility as a Service are converging to reshape transportation systems.
  • Blockchain-based intelligent transportation systems target secure, reliable, and autonomous ecosystems with optimized infrastructure and resource use.One study presents a seven-layer conceptual model characterizing the architecture and major components.
  • An offline blockchain storage system stores users’ sensitive data and later shares it through encryption keys associated with particular car clusters.The system uses client and server applications and assumes secure smartphone and server configurations.
  • Blockchain-integrated big data can accelerate IoT platforms and digital applications while supporting peer-to-peer energy trading and decentralization.
  • A distributed key-management system uses a utility–smart-meter key agreement and distributed multicaste key management for smart-grid group communication.
  • Storj encrypts files client-side, splits them into shards, stores three backups, and restricts client access to improve security over centralized cloud services.Its storage contracts use peer-to-peer authentication between providers and users.

2) Omnilytics:

The survey highlights blockchain-big data projects spanning analytics, trading, supply chains, decentralized storage, and data marketplaces.

  • 2) Omnilytics:: Omnilytics integrates blockchain, big data analytics, ML, and AI for sales, marketing, and merchandising insights.Its services include competitor benchmarking, trend analysis, and pricing analysis.
  • Rubix:: Rubix combines decentralized trading with investment data analytics to rank traders by prediction accuracy.Blockchain verifies traders and supports incentives based on prediction performance.
  • Provenance:: Provenance gathers and shares trusted product information across supply-chain participants, including producers, manufacturers, certifiers, and customers.Consumers can access product origin and journey information.
  • FileCoin:: FileCoin creates a decentralized storage marketplace where users buy and sell storage using cryptocurrency.Miners provide storage and services and submit proof-of-space-time when mining blocks.
  • Datum:: Datum enables anonymous, secure storage and marketplace-based sharing or selling of data from social networks, IoT devices, and wearables.Users receive unique Datum IDs managed through its mobile application.

V. RESEARCH CHALLENGES AND FUTURE DIRECTIONS

The survey identifies security, computational complexity, scalability, and resource constraints as central challenges when integrating blockchain with big data.

  • Security in Blockchain:: Blockchain-big data integration creates opportunities for data validation and management but must address security and analytics-model concerns.Blockchain keeps transaction history, while big data introduces security issues and complex analytics requirements.
  • Security in Blockchain:: 51% attacks and double-spending remain relevant blockchain security concerns despite decentralization and smart-contract protections.A 51% attack requires control of about 50% of computational power, while smart contracts reinforce defenses against double spending.
  • Security in Blockchain:: Permissioned blockchain can guarantee data provenance, while resource-constrained IoT environments require lightweight blockchain designs.Traditional cryptographic infrastructure may not adequately secure IoT environments accumulating data from varied sources.
  • Scalability and Computation:: Blockchain scalability has a trade-off: distributed architectures support trusted transactions, but longer chains increase storage and processing loads.Cryptographic operations and block creation impose additional computational requirements on simple, resource-constrained nodes.
  • Future Directions:: Security measures should improve protection without imposing excessive computational resources, and blockchain analytics should address increasing data complexity.Public or private blockchain selection, security measures, and processing capabilities should be considered before integration.

2) Standardization:

The survey presents standardization as important for blockchain interoperability and adoption, while emphasizing that big data’s complexity complicates integration and processing.

  • 2) Standardization:: Blockchain adoption has been hindered by interoperability issues despite its ability to support secure digital-asset transactions across banks.Standardization efforts address privacy, taxonomy, smart contracts, security, interoperability, governance, and use cases.
  • 2) Standardization:: ISO/TC 307, ITU, and W3C contribute to developing standards and best practices for blockchain and distributed-ledger technologies.The initiatives cover blockchain applications, services, implementation practices, and related research standards.
  • 2) Standardization:: Blockchain standardization can follow anticipatory, participatory, or responsive approaches depending on when standards are developed and adopted.These approaches correspond to periods before acceptance, during implementation, and after technology uptake.
  • 2) Standardization:: Blockchain can support cross-domain data sharing across data silos, but adoption requires appropriate standards and guidelines.The survey links standards to smoother operation of blockchain for big data.
  • Big Data Complexity:: Big data integration must account for inaccessible or dirty data, heterogeneous sources, complex structures, processing demands, and energy use.The survey recommends evaluating relationships among data complexity, computation, energy consumption, and efficiency.

B. Future Directions

Future directions emphasize adaptive and scalable blockchain architectures, integration with cloud and edge technologies, and compatibility across heterogeneous systems.

  • B. Future Directions: Integrating big data with blockchain can create unexpected computational complexity and poor system performance.Both big data processing and decentralized ledgers require substantial computational capability.
  • Adaptive Blockchain Design for Big Data:: Adaptive blockchain designs aim to reduce computational power requirements for real-time and large-scale big data.Lightweight blockchains target real-time data, while scalable blockchains target large-scale data.
  • Adaptive Blockchain Design for Big Data:: BigChainDB combines blockchain with distributed databases to improve scalability through faster querying mechanisms.It supports 1-million writes for a second, sub-second latency, petabyte capacity, and public or private permissioning.
  • Adaptive Blockchain Design for Big Data:: HBasechainDB implements blockchain on Hadoop’s distributed database and achieves linear scaling, sub-second latency, and higher transaction throughput.Its suitability is limited to organizations operating on the Hadoop ecosystem.
  • B. Future Directions: Future systems should address decentralization, security, privacy, transparency, interoperability, latency, and resource constraints across 5G, cloud, MEC, and SDN.Examples include private blockchain for BIM provenance, privacy-preserving MEC routing, and smart-contract-based IoT data exchange.

VI. CONCLUSIONS

The survey examines blockchain–big data services and applications, identifying technical challenges and future directions. It concludes that blockchain can help secure and manage accumulated big data while supporting integrity and analytics-driven insights.

  • The article surveys blockchain for big data acquisition, storage, analytics, and privacy preservation across major vertical applications.
  • The survey points out future directions intended to spur further research on blockchain–big data services and applications.
  • The review identifies technical challenges in integrating and deploying robust blockchain frameworks for big data.
  • Blockchain can manage and secure substantial big-data accumulations and services through decentralized, immutable ledgers that ensure data integrity.
  • Big-data analytics can provide better insights for making valuable predictions from massive data accumulations.
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