How do I anonymize data before sending it to W&B for a privacy-sensitive project?

I've been architecting a machine learning pipeline for a client in the healthcare sector, where the training data contains Protected Health Information (PHI). The model performance tracking and experiment management capabilities of Weights & Biases are nearly ideal for our workflow, but the data privacy requirements present a significant hurdle. The client's compliance team has a strict mandate: no raw PHI, or even reversible pseudonymization, can leave the on-premise environment. We must send *only* fully anonymized or synthetic representations to W&B's cloud service.

My current approach involves a two-stage logging abstraction, but I'm concerned about introducing excessive latency or losing the granularity that makes W&B useful. I want to open a discussion on the specific techniques and engineering patterns others have employed to balance W&B's rich telemetry with stringent data anonymization.

Here is my current prototype pipeline's logging layer, which performs local anonymization before any network call:

```python
import wandb
import hashlib
import re

class AnonymizedWandbLogger:
def __init__(self, salt="project_specific_secret"):
self.salt = salt
# Initialize wandb but defer run start until config is scrubbed
self.config = {}

def _hash_string(self, original):
"""One-way hash for categorical identifiers."""
salted = original + self.salt
return hashlib.sha256(salted.encode()).hexdigest()[:16]

def _mask_numbers(self, text):
"""Replace numeric sequences with masked tokens."""
return re.sub(r'd+', '[NUM]', text)

def log_metrics(self, metrics_dict):
# Metrics are usually safe, but we audit keys.
wandb.log(metrics_dict)

def log_text(self, key, text):
processed = self._mask_numbers(text)
processed = self._hash_string(processed) # Only if text is an identifier
wandb.log({key: processed})

def log_artifacts(self, artifact_name, file_path):
# This is the trickiest part. We must not upload raw data files.
# Our solution: locally generate derived, anonymized features
# and serialize those to a new file, then log that.
derived_features_path = self._generate_anonymized_features(file_path)
artifact = wandb.Artifact(artifact_name, type='processed_data')
artifact.add_file(derived_features_path)
wandb.log_artifact(artifact)
```

Key challenges I'm still grappling with:

* **Image & Text Data**: For medical imaging or clinical notes, true anonymization is non-trivial. Simply stripping DICOM headers is insufficient. Are there established libraries or local preprocessing pipelines that output a "safe" version suitable for W&B artifact logging?
* **Artifact Lineage**: If we upload only anonymized derivatives, the artifact lineage points to the derived file, not the source. This breaks reproducibility. How do others maintain a traceable yet compliant chain of custody?
* **Performance Overhead**: The local processing (hashing, masking, feature derivation) adds latency. Has anyone benchmarked the impact of running, for instance, a full image anonymization filter (like pixel-level face/body part redaction) versus a sampling strategy for logging?
* **Config Privacy**: Even the `wandb.init(config=)` can leak sensitive info if it contains paths, dataset names, or hyperparameters tied to patient cohorts. What schema do you use to sanitize the config dictionary systematically?

I am particularly interested in solutions that are automated and low-touch for researchers, as manual steps are both a risk and a bottleneck. The trade-off between utility of logged data and privacy guarantee is the core engineering problem here.

--perf