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Yayın A deployment-oriented privacy-preserving CTI framework: integrating PIR, federated learning, differential privacy, and practical hardenings(Institute of Electrical and Electronics Engineers Inc., 2026) Çamalan, Emre; Çeliktaş, BarışThreat Intelligence Platforms (TIPs) enable organizations to share indicators of compromise (IoCs), yet the operational CTI lifecycle exposes multiple, largely independent privacy surfaces: query content and access-pattern leakage during IoC lookup, gradient and membership inference risks during collaborative model training, and residual metadata side-channels in network traffic. Existing work addresses these surfaces in isolation; no prior framework orchestrates their joint mitigation within a single, deploymentoriented CTI pipeline under explicit guarantee boundaries. We present a prototype workflow-level privacy orchestration for cyber threat intelligence that coordinates four mechanisms across the query-learn-update lifecycle: (i) Private Information Retrieval (PIR) to hide queried IoC indices, (ii) cross-silo federated learning (FL) to keep raw CTI data local, (iii) a formal client-level Differential Privacy (DP) mechanism for federated model training to protect against inversion and membership inference attacks, and (iv) practical privacy hardenings, namely fixed-shape PIR batching (a traffic-shaping mechanism, not a cryptographic PIR guarantee) and secure aggregation simulated under an honest-but-curious coordinator assumption, to mitigate residual side-channel leakage. The contribution is therefore one of CTI-specific workflow orchestration and systematic evaluation, not of new cryptographic primitives: formal (ε, δ) guarantees apply exclusively to the differentially private federated learning component, while the remaining mechanisms serve as deployment-oriented hardenings under stated assumptions. We implement a working prototype over a two-million-row AbuseIPDB-style IoC dataset. Under a two-server non-colluding assumption, PIR queries complete in approximately 40 seconds with 16MB transfer per fixed batch. Local Random Forest and Logistic Regression baselines reach 89.0% and 77.00% accuracy, respectively, while federated variants with DP-FedAvg (gradient clipping and RDP-based privacy accounting) demonstrate a quantified privacy–utility trade-off across multiple noise levels. A corrected canonical single-round (T=1) baseline establishes the reconciled reference operating point; reviewer-driven multi-round experiments (T ∈ {1, 10, 20}) and an auxiliary clip-norm sensitivity analysis (C ∈ {0.5, 1.0, 2.0}) further characterize how privacy budgets, model utility, and training stability evolve beyond the single-round setting, with all (ε, δ) values computed via RDP composition for the corresponding configuration. The framework aligns with recent advances in secure aggregation and privacy-preserving CTI analytics, and is designed to be compatible with GDPR, CCPA, ISO/IEC 27701, and NIST 800-53 privacy principles, demonstrating prototype-level feasibility for regulation-aware CTI collaboration across organizations.
