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Database Systems and Data Protection
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Database Systems and Data Protection

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 916

Editors

Dr. Jinbao Wang

E-Mail Website
Guest Editor
Department of Computer Science, Harbin Institute of Technology, Weihai 264209, China
Interests: database; big data management and analysis; data privacy
Dr. Xu Zheng

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: data privacy; artificial intelligence; Internet of things
Special Issues, Collections and Topics in MDPI journals
Dr. Dan Yin

E-Mail Website
Guest Editor
Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Interests: database; data mining and analysis; graph neural networks

Special Issue Information

Dear Colleagues,

Database systems provide the foundational abstractions for managing data, including storage and indexing, query processing, transaction management, and consistency guarantees. As data volumes grow and database deployments become increasingly distributed and autonomous, data protection has emerged as an integral component of database system functionality, closely coupled with core design choices and execution semantics.

In modern database systems, protection-related properties such as controlled data visibility, isolation, correctness, and reliable recovery are inherently shaped by system mechanisms including data placement, replication, indexing, concurrency control, and distributed coordination. Rather than being enforced solely as external safeguards, data protection is increasingly realized through database-native abstractions and algorithms that govern how data is accessed, processed, and maintained across failures and heterogeneous environments.

Recent research demonstrates that advances in database system architecture increasingly incorporate protection considerations into fundamental components such as storage engines, query optimizers, execution models, and transaction protocols. These designs enable database systems to balance performance, scalability, and availability while providing well-defined protection semantics as part of normal system operation.

This Special Issue focuses on database systems and data protection as integral aspects of data management. It aims to bring together contributions on database system architectures, algorithms, and implementations, as well as studies that investigate data protection mechanisms within database systems, whether embedded into core functionalities or realized as system-level extensions.

By complementing the existing literature that often treats database system performance and data protection mechanisms separately, this topical collection emphasizes system-centric approaches that advance database system design while addressing data protection within database environments.

Topics of interest include, but are not limited to, the following:

  • Database system architectures with built-in protection semantics;
  • Storage engines and indexing structures;
  • Data placement, sharding, and replication mechanisms;
  • Transaction processing, concurrency control, and recovery;
  • Distributed consistency and coordination protocols;
  • Query processing and optimization;
  • Fault tolerance and availability in database systems;
  • Resource management and workload isolation;
  • System support for controlled data access;
  • Trade-offs among performance, consistency, and protection;
  • Data privacy protection;
  • Data security.

Dr. Jinbao Wang
Dr. Xu Zheng
Dr. Dan Yin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • database
  • storage
  • indexing
  • query processing
  • concurrency control
  • data privacy
  • data security

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Published Papers (2 papers)

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Research

19 pages, 380 KB  
Article
Do Open-Weight LLMs Respect Minimum-Necessary Access in Text-to-SQL? An Automated Audit on EHR Benchmarks
by Alberto Ballesteros-Rodríguez, Lino González-García, Miguel-Angel Sicilia and Elena García-Barriocanal
Electronics 2026, 15(15), 3252; https://doi.org/10.3390/electronics15153252 - 23 Jul 2026
Viewed by 263
Abstract
Large language models are increasingly deployed as natural-language interfaces to clinical databases, yet their evaluation has focused almost exclusively on query correctness rather than access minimality. Data-protection frameworks such as HIPAA §164.502(b) and the GDPR data-minimisation principle require that personal data be [...] Read more.
Large language models are increasingly deployed as natural-language interfaces to clinical databases, yet their evaluation has focused almost exclusively on query correctness rather than access minimality. Data-protection frameworks such as HIPAA §164.502(b) and the GDPR data-minimisation principle require that personal data be accessed only to the extent necessary for the immediate purpose. This paper presents the first empirical audit of open-weight LLMs on this access-scope dimension, using the EHRSQL 2024 benchmark over MIMIC-IV. We measure deviations from minimum-necessary access via three static metrics computable without executing queries: Sensitive Column Exposure (SCE), Table Over-Join Rate (TOJ), and Aggregate Identifier Exposure (AIE). Unlike prior work on adversarial attacks, we study default model behaviour under ordinary, benign queries—the over-reach we document requires no attacker and is present from the first interaction. Among twelve open-weight models, a non-trivial fraction fail a basic SQL-generation viability threshold; among nine viable models, SQL-specialised training paradoxically increases over-reach while domain-adapted medical variants achieve the lowest exposure. Schema redaction largely eliminates column-level exposure but increases table-level over-reach for most models. Random in-context demonstrations produce statistically significant over-reach regressions in specific models; BM25-based retrieval resolves most of these failures, though a residual aggregate-identifier exposure risk persists for one model family. Full article
(This article belongs to the Special Issue Database Systems and Data Protection)
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49 pages, 1066 KB  
Article
Scalable and Trusted Metadata-Coordinated Tiered Off-Chain Storage with Dynamic On-Chain Mapping for Recovery-Safe and Low-Latency IoT Data Management
by Weiping Yu, Weihan Wang, Mingyuan Yan, Keyang He, Zhe Yu, Wenpeng Xing, Liyuan Liu and Meng Han
Electronics 2026, 15(13), 2806; https://doi.org/10.3390/electronics15132806 - 25 Jun 2026
Viewed by 236
Abstract
Blockchain-assisted off-chain storage for IoT must simultaneously manage low-latency tiered data placement, trusted and dynamic on-chain mapping, migration consistency, and failure recovery—four concerns that existing designs address in isolation. Tiered storage systems optimize placement without modeling the scalable coordination cost of keeping object–location [...] Read more.
Blockchain-assisted off-chain storage for IoT must simultaneously manage low-latency tiered data placement, trusted and dynamic on-chain mapping, migration consistency, and failure recovery—four concerns that existing designs address in isolation. Tiered storage systems optimize placement without modeling the scalable coordination cost of keeping object–location bindings trustworthy, while blockchain-metadata studies assume static storage topologies with no dynamic tier migration. This paper presents a scalable and trusted metadata-coordinated tiered off-chain storage framework, which bridges traditional trust systems (e.g., legacy authentication) with blockchain networks powered by Proof of Capacity (PoC) consensus. In this framework, adaptive heat-driven placement, dynamic on-chain mapping evolution with batched commitment, migration-aware redirect control, and rollback-safe recovery operate as a single coordinated workflow, with the five-stage write–verify–commit–redirect–retire pipeline acting as a lightweight coordination protocol that maintains ordered and atomic state transitions under message loss, out-of-order delivery, and single-node failures. The distinctive contribution lies in the framework’s coupled control: every placement decision propagates through a verifiable metadata path that can be audited and, when necessary, rolled back. Simulation across multiple workload patterns shows that the proposed method reduces average access latency by 28% and raises the hot-tier hit ratio from 0.19 to 0.65 relative to a dynamic baseline without trusted mapping coordination under the simulated registry write cost. To achieve high-throughput mapping operations, batched on-chain commitment cuts metadata transactions by 50× at the cost of a tunable mapping freshness delay. The framework scales from 1 k to 50 k managed objects, effectively managing tens of millions of bytes of data (10+ MB scale) without disproportionate overhead growth; beyond this scale, hot-tier capacity rather than coordination becomes the dominant bottleneck, and smarter predictive placement becomes the natural next lever. All tested fault types achieve 100% rollback success with sub-millisecond local data plane interruption; audit-visible recovery depends on the assumed chain finality delay and, for heavily regulated IoT domains, such as finance and healthcare, should be treated as the operationally binding recovery time objective. These results, together with extended evaluations—including asymmetric write latency stress, coordination ablation, tail latency analysis, and benefit–complexity assessment—provide quantitative evidence that scalable, dynamic mapping coordination can be integrated into tiered off-chain data management at an acceptable and measurable operational cost under the simulated configuration. Full article
(This article belongs to the Special Issue Database Systems and Data Protection)
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