---
language:
- en
license: mit
pretty_name: DAG Remediation Traces
tags:
- phi
- de-identification
- clinical-nlp
- privacy
- dag
- remediation-planning
- graph
- planning
- healthcare
- hipaa
- multimodal
- synthetic
- budget-constrained
- topology
- dependency-injection
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- other
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.jsonl
  - split: test
    path: data/test.jsonl
- config_name: hard
  data_files:
  - split: test
    path: data/hard.jsonl
---

# DAG Remediation Traces

**Author:** Venkata Krishna Azith Teja Ganti
**Part of the [ExposureGuard PHI Re-identification Risk Ecosystem](https://huggingface.co/vkatg)**

Input/output execution traces for budget-constrained PHI remediation planning over multimodal clinical records. Each record pairs a patient risk profile with a complete DAG planning trace: which actions were selected, which dependency injections fired, the topological execution order, and the final residual risk and cost.

Use this to train or benchmark alternative remediation planners, study cost/risk tradeoffs, or evaluate DAG construction under budget constraints across text, image, audio, and EHR modalities.

## Splits

| Split | Config | Records | plan_ready | below_threshold |
|-------|--------|---------|-----------|----------------|
| train | default | 6,000 | 5,051 (84.2%) | 949 (15.8%) |
| test | default | 1,500 | 1,277 (85.1%) | 223 (14.9%) |
| test | hard | 1,000 | 1,000 (100%) | 0 |

The `hard` config fixes input risk between 0.75 and 0.99 and caps budget at 0.60, creating a stress-test set where every record requires planning under tight constraints.

### Plan quality

Numbers are shown for all records and for planned-only (records where the planner actually ran). The gap between the two reflects the 15.8% of default train records that were already below the risk threshold and received empty plans. The hard split has no below-threshold records so both columns are the same.

| Metric | default all | default planned | hard all | hard planned |
|--------|------------|----------------|----------|--------------|
| avg actions per plan | 3.47 | 4.12 | 5.50 | 5.50 |
| avg topological depth | 3.47 | 4.12 | 5.50 | 5.50 |
| max topological depth | 10 | 10 | 10 | 10 |
| avg residual risk | 0.0420 | 0.0261 | 0.0429 | 0.0429 |
| avg total cost | 0.3093 | 0.3093 | 0.4678 | 0.4678 |
| plans reaching residual = 0.0 | 74.0% | 74.0% | 66.4% | 66.4% |
| avg risk reduction | 96.5% | 96.5% | 94.8% | 94.8% |
| median risk reduction | 100.0% | 100.0% | 100.0% | 100.0% |
| records with injected deps | 33.6% | 33.6% | 67.9% | 67.9% |
| unique execution sequences | 38 | 38 | -- | -- |

The injected deps rate nearly doubles in the hard split (33.6% to 67.9%) -- tighter budgets under high risk force more complex dependency chains.

These metrics are self-reported by the generator. They describe how the planner performs against its own scoring logic, not against externally validated PHI risk reduction.

### Input risk distribution (default train)

| Metric | Value |
|--------|-------|
| mean | 0.5191 |
| stdev | 0.2701 |
| min / max | 0.0502 / 0.9899 |

### Input risk distribution (hard)

| Metric | Value |
|--------|-------|
| mean | 0.8692 |
| min / max | 0.7500 / 0.9898 |

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("vkatg/dag_remediation_traces")
train = ds["train"]
test  = ds["test"]
```

```python
import json

with open("data/train.jsonl") as f:
    records = [json.loads(line) for line in f]

planned = [r for r in records if r["status"] == "plan_ready"]
print(len(planned), "planned records")
print("avg residual risk:", sum(r["estimated_residual_risk"] for r in planned) / len(planned))
```

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `patient_id` | string | Synthetic patient identifier (P-000000 format) |
| `input.risk_score` | float | Re-identification risk score (0.0 to 1.0) |
| `input.retok_prob` | float | Re-tokenization probability (influences retokenize_text scoring) |
| `input.active_modalities` | list | Active data modalities: text, image, audio, ehr |
| `input.budget` | float | Total cost budget for the remediation plan |
| `plan` | list | Ordered list of actions with priority, cost, risk_delta, deps |
| `trace.selected_actions` | list | Actions chosen by the scorer before dependency injection |
| `trace.injected_deps` | list | Dependencies automatically injected to satisfy action prerequisites |
| `trace.topo_order` | list | Final topological execution order |
| `trace.score_breakdown` | dict | Per-action priority scores |
| `estimated_residual_risk` | float | Risk remaining after all actions execute |
| `total_cost` | float | Sum of costs across all actions including injected deps |
| `status` | string | `plan_ready` or `below_threshold` |

## Action Catalog (14 actions)

| Action | Cost | Risk Reduction | Modalities |
|--------|------|---------------|-----------|
| `mask_direct_id` | 0.05 | 0.35 | text, image |
| `generalize_dob` | 0.03 | 0.12 | text, ehr |
| `suppress_geo` | 0.04 | 0.10 | text, ehr |
| `redact_image_face` | 0.12 | 0.20 | image |
| `redact_image_tag` | 0.08 | 0.14 | image |
| `strip_audio_voice` | 0.15 | 0.18 | audio |
| `anon_audio_content` | 0.10 | 0.12 | audio |
| `drop_rare_code` | 0.06 | 0.09 | ehr |
| `k_anon_table` | 0.20 | 0.22 | ehr, text |
| `perturb_numerics` | 0.07 | 0.07 | ehr |
| `retokenize_text` | 0.09 | 0.11 | text |
| `cross_modal_unlink` | 0.18 | 0.28 | all |
| `federated_noise` | 0.25 | 0.15 | all |
| `audit_log_purge` | 0.02 | 0.03 | all |

### Dependency graph

Some actions require others to run first:

- `retokenize_text` requires `mask_direct_id`
- `k_anon_table` requires `generalize_dob`, `suppress_geo`, `drop_rare_code`
- `cross_modal_unlink` requires `redact_image_face`, `strip_audio_voice`, `mask_direct_id`
- `federated_noise` requires `k_anon_table`, `cross_modal_unlink`
- `audit_log_purge` requires `federated_noise`
- `redact_image_tag` requires `redact_image_face`
- `anon_audio_content` requires `strip_audio_voice`

When a selected action has unmet dependencies, those dependencies are injected automatically. 33.6% of planned records include at least one injected dependency.

### Action frequency (train)

| Action | Occurrences |
|--------|------------|
| mask_direct_id | 4,208 |
| generalize_dob | 4,175 |
| suppress_geo | 3,312 |
| cross_modal_unlink | 1,388 |
| retokenize_text | 1,156 |
| redact_image_tag | 950 |
| audit_log_purge | 352 |
| redact_image_face | 306 |
| strip_audio_voice | 233 |
| drop_rare_code | 189 |

## Sample Record

```json
{
  "patient_id": "P-000001",
  "input": {
    "risk_score": 0.9051,
    "retok_prob": 0.2147,
    "active_modalities": ["ehr"],
    "budget": 0.86
  },
  "plan": [
    {"action": "generalize_dob", "priority": 8.1366, "cost": 0.03, "risk_delta": 0.12, "deps": []},
    {"action": "suppress_geo",   "priority": 5.0854, "cost": 0.04, "risk_delta": 0.10, "deps": []},
    {"action": "cross_modal_unlink", "priority": 3.1642, "cost": 0.18, "risk_delta": 0.28, "deps": ["redact_image_face", "strip_audio_voice", "mask_direct_id"]}
  ],
  "trace": {
    "selected_actions": ["generalize_dob", "suppress_geo", "cross_modal_unlink", "drop_rare_code", "audit_log_purge", "k_anon_table"],
    "injected_deps": ["redact_image_face", "strip_audio_voice", "mask_direct_id", "federated_noise"],
    "topo_order": ["generalize_dob", "suppress_geo", "redact_image_face", "strip_audio_voice", "mask_direct_id", "cross_modal_unlink", "drop_rare_code", "federated_noise", "audit_log_purge", "k_anon_table"],
    "score_breakdown": {"generalize_dob": 8.1366, "suppress_geo": 5.0854, "cross_modal_unlink": 3.1642}
  },
  "estimated_residual_risk": 0.0,
  "total_cost": 1.1,
  "status": "plan_ready"
}
```

## Planner Logic

The scorer ranks actions by a return-on-investment signal:

```
score = (reduces * modality_weight + retok_bonus) / base_cost * log(1 + risk * 5)
```

`modality_weight` is sampled per record (0.8 to 1.5) for a random subset of active modalities, reflecting variable sensitivity across data types. `retok_bonus` (+0.15) fires for `retokenize_text` when `retok_prob > 0.55`.

Selection adds actions greedily by score until the budget is hit or the risk reduction target is met (target = max(0, risk - 0.20), minimum spend 0.10). Dependency injection runs after selection to ensure topological validity.

## Reproduce

```bash
python generate_dag_traces.py
```

The default train/test splits are deterministic at seed 7. The hard split uses seed 42 with risk sampled from [0.75, 0.99] and budget from [0.30, 0.60].

## Related Models

| Model | Role |
|-------|------|
| [vkatg/exposureguard-dagplanner](https://huggingface.co/vkatg/exposureguard-dagplanner) | Model trained/evaluated on this dataset |
| [vkatg/exposureguard-fedcrdt-distill](https://huggingface.co/vkatg/exposureguard-fedcrdt-distill) | Produces `risk_score` and `retok_prob` inputs |
| [vkatg/exposureguard-dcpg-encoder](https://huggingface.co/vkatg/exposureguard-dcpg-encoder) | Alternative source of `risk_score` |

## Related Datasets

- [vkatg/streaming-phi-deidentification-benchmark](https://huggingface.co/datasets/vkatg/streaming-phi-deidentification-benchmark)
- [vkatg/phi-audit-trace-benchmark](https://huggingface.co/datasets/vkatg/phi-audit-trace-benchmark)

## Citation

```bibtex
@misc{ganti2025exposureguard,
  title        = {ExposureGuard: Cross-Modal PHI Re-identification Risk Scoring with DCPG and Federated CRDT Distillation},
  author       = {Ganti, Venkata Krishna Azith Teja},
  year         = {2025},
  doi          = {10.5281/zenodo.18865882},
  howpublished = {\url{https://huggingface.co/vkatg}}
}
```

## License

MIT. Fully synthetic data. Contains no real patient information.