PII-TRACE: Detecting personal data before it leaves the device

Perplexity introduces PII-TRACE, a public benchmark for testing personally identifiable information detection in long, multilingual, multi-turn conversations, and PII-Tracer, a 0.6-billion-parameter local model intended to act as a privacy gate in hybrid Mac-based AI systems. Hybrid compute routes research and planning to cloud models while retaining private files and sensitive context locally, but its privacy protections depend on reliably detecting every occurrence of recurring PII before data leaves the device. PII-TRACE contains 13,148 synthetic conversations in 13 languages, preserves dialogue structure and repeated identifiers, and evaluates both character-level detection and whether all mentions of an identifier are found across turns. PII-Tracer uses bidirectional attention, token-level BIOES span tagging, a sensitive-conversation auxiliary classifier, and constrained decoding to identify nine PII categories locally. In evaluations against 11 other detectors, it reportedly achieved the highest character-level F1 score and stronger coverage of recurring and cross-turn identifiers than larger cloud-hosted frontier models, while sliding-window processing substantially improved its performance on very long conversations. The model also outperformed the OpenAI Privacy Filter on five conventional external PII benchmarks, according to the reported character-level F1 results, and both the benchmark and model are available through Hugging Face.

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