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Securing your experience...
Securing your experience...
Perform network analytics and anomaly detection on encrypted traffic without decrypting sensitive mobile data.
Network Data Analytics Functions (NWDAF) in 5G need to analyze massive amounts of mobile network data to predict issues, detect anomalies, and optimize performance. But this data is sensitive: subscriber lists, traffic patterns, service usage. Data breaches expose competitive and privacy vulnerabilities.
Network metrics are encrypted before reaching the NWDAF. ML models run on encrypted data. Anomaly detection and optimization happen without decryption. Network operators get analytics without exposing raw traffic.
Base stations collect encrypted traffic metrics.
NWDAF ingests encrypted metrics directly—no decryption step.
ML models (logistic regression, anomaly detection) run on ciphertext.
Results are encrypted and sent back to operator. Operator decrypts with their key.
Operators see: network anomalies, traffic predictions, performance optimizations—all derived from encrypted data.
Network data stays encrypted throughout the pipeline. Operators trust the NWDAF with encrypted data, not plaintext. Performance predictions are fast because FHE on encrypted metrics is nearly as fast as plaintext computation.
Network metrics are encrypted at the source. NWDAF never sees plaintext traffic patterns or subscriber details.
Logistic regression and tree-based models run directly on encrypted features. No decryption in the compute pipeline.
Unsupervised ML detects network anomalies and unusual traffic patterns without exposing individual data points.
Network operators control encryption keys. NWDAF is a untrusted compute service. Analytics are delivered encrypted.