Data Privacy and Security in Blockchain, Decentralised Storage and IoT Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 800

Special Issue Editor


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Guest Editor
School of Computing, Engineering & Intelligent Systems, Ulster University, Londonderry BT48 7JL, UK
Interests: blockchain methods; privacy-preserving techniques; decentralised storage systems; secure data sharing; data integrity and auditability; consensus mechanisms

Special Issue Information

Dear Colleagues,

The rapid advancements being made in blockchain technology, decentralised storage systems, and privacy-preserving technologies have opened up new paths for secure and transparent data management across various sectors. As concerns around data security, privacy, and integrity grow, these technologies offer promising solutions by ensuring immutable, transparent, and private data exchanges.

This Special Issue seeks to gather high-quality research articles, reviews, and position papers that explore the intersection of blockchain, decentralised storage, and privacy-preserving techniques. The goal is to address the current challenges and future opportunities in applying these technologies to enhance the security, integrity, and auditability of data.

In this Special Issue, original research articles and reviews are welcome.

Their research areas may include (but are not limited to) the following:

  • Blockchain architectures and methods;
  • Privacy-preserving techniques;
  • Decentralised storage systems (e.g., IPFS);
  • Secure data sharing and integrity;
  • Data auditability and transparency;
  • Blockchain and IoT security;
  • Legal, regulatory, and ethical considerations in privacy-preserving systems;
  • Privacy implications of decentralised applications (dApps);
  • The role of AI in enhancing privacy and security in blockchain ecosystems;
  • The interoperability of decentralised systems across sectors and industries.

I look forward to receiving your contributions.

Dr. Sarwar Sayeed
Guest Editor

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Keywords

  • blockchain
  • decentralised storage
  • data privacy
  • consensus mechanisms
  • IPFS
  • privacy-preserving techniques
  • data integrity
  • auditability

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Published Papers (1 paper)

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Research

23 pages, 2885 KB  
Article
AI-Controlled Modular Decoy Generation for Reconstruction-Resistant Hybrid and Multi-Cloud Storage Systems
by Munir Ahmed and Jiann-Shiun Yuan
Electronics 2026, 15(6), 1231; https://doi.org/10.3390/electronics15061231 - 16 Mar 2026
Viewed by 310
Abstract
Although cloud storage is widely trusted by users and enterprises, externally stored encrypted and fragmented data remain vulnerable to reconstruction and inference attacks following partial exposure. Existing decoy-based defenses often rely on static configurations or randomly generated artifacts that can be filtered during [...] Read more.
Although cloud storage is widely trusted by users and enterprises, externally stored encrypted and fragmented data remain vulnerable to reconstruction and inference attacks following partial exposure. Existing decoy-based defenses often rely on static configurations or randomly generated artifacts that can be filtered during adversarial analysis. This paper presents an Artificial Intelligence (AI)-controlled modular decoy generation method to enhance reconstruction resistance in distributed storage systems. The method operates as a system-agnostic post-fragmentation layer and does not require modification of encryption or storage architecture. Given encrypted fragments as input, decoys are generated using a supervised Extreme Gradient Boosting (XGBoost) regression model that adapts decoy quantity based on system telemetry and resource conditions. Decoys maintain statistical alignment with real encrypted fragments in size and Shannon entropy characteristics. To address scalability, the method is evaluated across small, medium, and large deployments comprising up to 413 externally exposed fragments and compared against fixed-ratio (10%, 20%) and randomized baselines. Experimental evaluation demonstrates increased adversarial uncertainty without altering legitimate reconstruction procedures or encryption mechanisms. Kolmogorov–Smirnov analysis indicates no statistically significant difference between AI-generated decoys and real fragments, whereas baseline decoys produce significant deviations in size and entropy distributions, supporting reconstruction resistance at scale in multi-cloud environments. Full article
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