Uploaded June 2026 | Updated September 2026, 3 weeks ago
NSDI '26 - PrvTel: Lightweight Models for Private and Accurate Telemetry Data Retention
Yajie Zhou, University of Maryland; Fuheng Zhao, University of Utah; Eric Wang, University of Maryland; Ayse K. Coskun, Boston University; Divyakant Agrawal and Amr El Abbadi, University of California, Santa Barbara; Zaoxing Liu, University of Maryland
Network operators rely on telemetry for performance and security analysis, but long-term retention at scale remains difficult due to privacy requirements, resource constraints, and the need for high-fidelity query answers. We present PrvTel, a framework for privacy-preserving telemetry retention. Instead of storing raw records, PrvTel learns a compact generative model using a domain-specialized variational autoencoder. It combines field-aware encodings for NetFlow and cloud telemetry with a correlation-aware objective to preserve cross-field dependencies. To enforce differential privacy (DP) without sacrificing utility, PrvTel injects structure-aware noise before training, rather than during gradient updates. We prove that PrvTel satisfies DP based on post-processing theorem. Across six real-world datasets and one synthetic workload, PrvTel improves query accuracy by up to 60% over prior DP-compliant generative baselines and reduces ownership cost by up to 50× compared to lossless retention.
View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions
NSDI '26 - PrvTel: Lightweight Models for Private and Accurate Telemetry Data Retention
Yajie Zhou, University of Maryland; Fuheng Zhao, University of Utah; Eric Wang, University of Maryland; Ayse K. Coskun, Boston University; Divyakant Agrawal and Amr El Abbadi, University of California, Santa Barbara; Zaoxing Liu, University of Maryland
Network operators rely on telemetry for performance and security analysis, but long-term retention at scale remains difficult due to privacy requirements, resource constraints, and the need for high-fidelity query answers. We present PrvTel, a framework for privacy-preserving telemetry retention. Instead of storing raw records, PrvTel learns a compact generative model using a domain-specialized variational autoencoder. It combines field-aware encodings for NetFlow and cloud telemetry with a correlation-aware objective to preserve cross-field dependencies. To enforce differential privacy (DP) without sacrificing utility, PrvTel injects structure-aware noise before training, rather than during gradient updates. We prove that PrvTel satisfies DP based on post-processing theorem. Across six real-world datasets and one synthetic workload, PrvTel improves query accuracy by up to 60% over prior DP-compliant generative baselines and reduces ownership cost by up to 50× compared to lossless retention.
View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions
