Before-and-After Quality Control for Batch Data Masking

Define the batch boundary explicitly

Separate records with an explicit delimiter rather than ambiguous blank space. ByteQuant uses a `---` line to process each piece independently and caps records per run, preventing long main-thread stalls.

If a record can contain that delimiter, choose a safer export form or process smaller groups. Structural mistakes such as splitting multiline fields can damage integrity before privacy review even begins.

• Document the delimiter rule.
• Test a small representative group.
• Respect record and character limits.

Before/after is a review surface, not proof

The comparison panel places raw input beside the latest visible output. It helps expose forgotten identifiers and over-masking, but an empty finding never proves that personal data is absent.

Obvious email or phone patterns are easier than contextual names, health details, or indirect identifiers. Combine pattern matching with human review, a data inventory, and purpose limitation.

• Sample every data class.
• Record false negatives and false positives separately.
• Verify placeholder consistency.

Pass the result safely

After completion, the trust card confirms that processing occurred on-device. A result can move to a JSON or CSV tool through a same-tab record that expires after twenty minutes; it is not cloud storage.

Recheck schema and row counts in the next tool. Downloading creates a new file whose retention and access you control. Use independent second-person review for high-risk datasets.