exposureguard-policynet

ExposureGuard-PolicyNet

Most PHI classifiers answer one question: is this PHI or not? This model answers a different question: given everything we know about this patient's exposure history so far, what should we do right now?

Five possible answers. One output per event.

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What it does

Takes a 17-dimensional exposure state vector and predicts the appropriate masking policy for the current event. The state captures cumulative risk, cross-modal linkage signals, pseudonym versioning, and modality context.

The adaptive_rewrite vs redact distinction is the key contribution. Both fire when risk crosses the threshold, but the cause matters. Cross-modal linkage means the same patient has appeared across two or more modalities and the records have been linked. That scenario calls for a full synthetic rewrite downstream via SynthRewrite-T5, not just redaction. Exposure accumulation, where risk built up within a single modality stream, calls for redact.

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Usage

from inference import predict

result = predict({
    "risk": 0.72,
    "risk_before": 0.43,
    "effective_units": 9,
    "units_factor": 0.362,
    "recency_factor": 0.81,
    "link_bonus": 0.20,
    "degree": 2,
    "confidence": 0.362,
    "pseudonym_version": 1,
    "triggered": True,
    "cross_modal_matches": ["text"],
    "modality": "asr",
})

print(result["policy"])      # adaptive_rewrite
print(result["confidence"])  # float
print(result["all_scores"])  # scores for all 5 policies

Loading directly from HuggingFace:

from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn

class PolicyNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(17, 64),  nn.LayerNorm(64),  nn.ReLU(), nn.Dropout(0.2),
            nn.Linear(64, 128), nn.LayerNorm(128), nn.ReLU(), nn.Dropout(0.2),
            nn.Linear(128, 64), nn.ReLU(),
            nn.Linear(64, 5),
        )
    def forward(self, x):
        return self.net(x)

weights = hf_hub_download("vkatg/exposureguard-policynet", "pytorch_model.bin")
model   = PolicyNet()
model.load_state_dict(torch.load(weights, map_location="cpu", weights_only=True))
model.eval()

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Input features (17 dimensions)

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Architecture

Linear(17->64) -> LayerNorm -> ReLU -> Dropout(0.2)
Linear(64->128) -> LayerNorm -> ReLU -> Dropout(0.2)
Linear(128->64) -> ReLU
Linear(64->5)

75KB weights file. No external dependencies beyond PyTorch.

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Training

6,000 synthetic patient scenarios, balanced at 1,200 samples per policy class. 85/15 train/val split, AdamW, cosine LR schedule, 60 epochs. Val accuracy: 99.89%. Per-class results: raw 100%, weak 99.7%, pseudo 99.8%, redact 100%, adaptive_rewrite 100%. The small number of weak/pseudo misclassifications comes from the adjacent risk ranges (0.35-0.64 and 0.65-0.84) where boundary cases are genuinely ambiguous.

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Where it fits

DCPG Risk Scorer
      |
  ExposureGuard-PolicyNet    <- this model
      |
  +---+-------------------+
  |                       |
adaptive_rewrite        redact / pseudo / weak / raw
  |
SynthRewrite-T5

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Related

• phi-exposure-guard: full system
• dcpg-cross-modal-phi-risk-scorer: produces the risk score and trigger inputs
• exposureguard-dcpg-encoder: graph encoder upstream
• exposureguard-fedcrdt-distill: federated risk scoring
• exposureguard-synthrewrite-t5: downstream rewriter for adaptive_rewrite decisions
• exposureguard-dagplanner: remediation planner
• streaming-phi-deidentification-benchmark: benchmark dataset
• multimodal-phi-masking-benchmark: PHI masking dataset

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Citation

@software{exposureguard_policynet,
  title  = {ExposureGuard-PolicyNet: Stateful Privacy Policy Selection for Streaming Multimodal Clinical Data},
  author = {Ganti, Venkata Krishna Azith Teja},
  doi    = {10.5281/zenodo.18865882},
  url    = {https://huggingface.co/vkatg/exposureguard-policynet},
  note   = {US Provisional Patent filed 2025-07-05}
}

Trained on fully synthetic data. Not validated for clinical use.