Techniques for improving the efficiency and security of Data Confidence Fabrics

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dc.check.date2026-12-31
dc.contributor.advisorSreenan, Cormac
dc.contributor.advisorRoedig, Utz
dc.contributor.authorKhalid, Asfaen
dc.contributor.funderEnterprise Ireland
dc.contributor.funderEuropean Commission
dc.contributor.funderScience Foundation Ireland
dc.date.accessioned2025-10-08T14:31:59Z
dc.date.available2025-10-08T14:31:59Z
dc.date.issued2025
dc.date.submitted2025
dc.description.abstractThis thesis presents novel techniques to improve the efficiency, scalability, and security of Data Confidence Fabrics (DCFs), a framework that ensures data authenticity and integrity in large scale, heterogeneous distributed systems by generating metadata at each point of data formation, processing, and transmission. Despite their strengths, DCFs face significant challenges, including excessive annotation and transactional overhead, which reduce scalability and efficiency, and metadata privacy risks, which compromise sensitive network information. To address these challenges, this research proposes methods that improve system scalability, enable efficient annotation retrieval, and protect sensitive network information, with a focus on the Alvarium Data Confidence Fabric, though the solutions are broadly applicable to other DCFs. A primary contribution of this work is addressing the efficiency and scalability challenges by reducing annotation overhead through compact annotation techniques, particularly annotation batching. By aggregating multiple annotations into a single ledger transaction, this approach minimizes redundancy, storage costs and ledger interactions. However, batching introduces complexity in retrieving individual annotations. To overcome this, two retrieval methods are proposed: Batch Keys, which use mapping tables to quickly locate individual annotations based on a Batch key, and Bloom Filters, which provide a low-overhead approach for efficiently verifying the presence of annotations. Another major focus of this work is mitigating metadata privacy risks, where adversaries could analyze annotations to infer network structures. To obscure network patterns, two privacy-preserving schemes, Hostname Mapping and Hostname Encryption, are introduced, with Hostname Encryption offering a more efficient and secure alternative. Additionally, the research highlights how timestamp metadata can be exploited to reconstruct network structures through clustering techniques. To mitigate this vulnerability, a timestamp obfuscation solution is proposed, introducing controlled randomness to disrupt predictable timing patterns and protect network confidentiality. In summary, the thesis introduces and evaluates methods that significantly enhance the efficiency, scalability, and security of DCFs. These contributions strengthen the practical deployment of DCFs in cloud-edge environments and provide a foundation for future research in secure and trustworthy data management across distributed systems.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationKhalid, A. 2025. Techniques for improving the efficiency and security of Data Confidence Fabrics. MResThesis, University College Cork.
dc.identifier.endpage120
dc.identifier.urihttps://hdl.handle.net/10468/17995
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectEnterprise Ireland (Grant EI IR-2022-0065)
dc.relation.projectinfo:eu-repo/grantAgreement/EC/HE::HORIZON-AG/101097560/EU/Collaborative edge-cLoud continuum and Embedded AI for a Visionary industry of thE futuRe/CLEVER
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/13/RC/2077_P2/IE/CONNECT_Phase 2/
dc.rights© 2025, Asfa Khalid
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDistributed systems
dc.subjectCloud-edge environments
dc.subjectData trustworthiness
dc.subjectData Confidence Fabrics
dc.subjectSecure data exchange platforms
dc.subjectZero Trust
dc.subjectProject Alvarium
dc.subjectData provenance
dc.subjectAnnotation
dc.subjectAuthentication and verification
dc.subjectTrust algorithms
dc.subjectTrust Score
dc.subjectData Confidence
dc.subjectSystem scalability
dc.subjectMetadata redundancy
dc.subjectStorage cost optimization
dc.subjectData compression techniques
dc.subjectCompact annotation techniques
dc.subjectCompact JSON annotation format
dc.subjectBatching
dc.subjectWorkload optimization
dc.subjectLow transactional overhead
dc.subjectOptimized system performance
dc.subjectLedger interactions
dc.subjectSystem efficiency
dc.subjectEfficient annotation retrieval
dc.subjectLedger querying and searching
dc.subjectBatch Keys
dc.subjectBloom Filters
dc.subjectSecurity
dc.subjectNetwork security
dc.subjectBlockchain forensics
dc.subjectDistributed ledgers
dc.subjectData analytics
dc.subjectPattern recognition
dc.subjectMetadata privacy
dc.subjectMetadata obfuscation techniques
dc.subjectHostname obfuscation
dc.subjectHostname anonymization
dc.subjectHostname Encryption
dc.subjectCorrelational analysis
dc.subjectNetwork structure obfuscation
dc.subjectNetwork topology
dc.subjectMachine learning
dc.subjectIntrusions and malware detection
dc.subjectVulnerability assessment
dc.subjectK-means clustering
dc.subjectSilhouette score evaluation
dc.subjectBidirectional analysis
dc.subjectTimestamp analysis
dc.subjectNetwork structure inference
dc.subjectTimestamp Obfuscation
dc.subjectNetworking
dc.titleTechniques for improving the efficiency and security of Data Confidence Fabrics
dc.title.alternativeImproving security and confidence in distributed edge environments
dc.typeMasters thesis (Research)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMSc - Master of Scienceen
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