Privacy-preserving cyber threat intelligence: a framework combining private information retrieval, federated learning, and differential privacy

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dc.authorid0009-0003-5878-5621
dc.authorid0000-0003-2865-6370
dc.contributor.authorÇamalan, Emreen_US
dc.contributor.authorÇeliktaş, Barışen_US
dc.date.accessioned2026-03-06T07:52:14Z
dc.date.available2026-03-06T07:52:14Z
dc.date.issued2025-09-21
dc.departmentIşık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programıen_US
dc.departmentIşık University, School of Graduate Studies, Master’s Program in Computer Engineeringen_US
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractThreat Intelligence Platforms (TIPs) are essential for sharing indicators of compromise (IoCs), but querying them can leak sensitive organizational data. We propose a privacy-preserving framework that combines Private Information Retrieval (PIR), Federated Learning (FL), and Differential Privacy (DP) to mitigate this risk. Our approach addresses both content-level and metadata-level privacy concerns while supporting collaborative learning across organizations. It ensures that sensitive query patterns remain hidden, local threat data never leaves organizational boundaries, and model updates are protected against inference attacks. The framework integrates with existing TIPs such as MISP and OpenCTI, requiring minimal operational changes. We implement a prototype using a simulated Abuse IP dataset and evaluate it on latency, accuracy, and communication overhead. The system supports private queries in under 300 ms and maintains over 95% model accuracy under DP noise. These results indicate that strong privacy can be achieved with minimal performance trade-offs, making the approach viable for real-world CTI environments.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationÇamalan, E. & Çeliktaş, B. (2025). Privacy-preserving cyber threat intelligence: a framework combining private information retrieval, federated learning, and differential privacy. Paper presented at the International Conference on Computer Science and Engineering, UBMK, 2025, 1525-1530. doi:https://doi.org/10.1109/UBMK67458.2025.11206831en_US
dc.identifier.doi10.1109/UBMK67458.2025.11206831
dc.identifier.endpage1530
dc.identifier.isbn9798331599751
dc.identifier.issn2521-1641
dc.identifier.issue2025
dc.identifier.scopus2-s2.0-105030881009
dc.identifier.scopusqualityN/A
dc.identifier.startpage1525
dc.identifier.urihttps://hdl.handle.net/11729/7103
dc.identifier.urihttps://doi.org/10.1109/UBMK67458.2025.11206831
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇamalan, Emreen_US
dc.institutionauthorÇeliktaş, Barışen_US
dc.institutionauthorid0009-0003-5878-5621
dc.institutionauthorid0000-0003-2865-6370
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofInternational Conference on Computer Science and Engineering, UBMKen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrencien_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDifferential privacyen_US
dc.subjectFederated learningen_US
dc.subjectPrivate information retrievalen_US
dc.subjectSecure data retrievalen_US
dc.subjectThreat intelligenceen_US
dc.subjectDistributed computer systemsen_US
dc.subjectInformation retrievalen_US
dc.subjectPrivacy-preserving techniquesen_US
dc.subjectQuery processingen_US
dc.subjectSensitive dataen_US
dc.subjectContent levelen_US
dc.subjectCyber threatsen_US
dc.subjectData retrievalen_US
dc.subjectDifferential privaciesen_US
dc.subjectOrganisationalen_US
dc.subjectPrivacy preservingen_US
dc.subjectSecure dataen_US
dc.subjectEconomic and social effectsen_US
dc.titlePrivacy-preserving cyber threat intelligence: a framework combining private information retrieval, federated learning, and differential privacyen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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