Description
Label a dataset's supplied hash-algorithm and salt-reference metadata.
Data workflows require explicit training scope, feature/label availability, missingness codes, group assignments and evaluation direction. Supplied training statistics are not proof that preprocessing avoided leakage. A local target-encoding term or retransformed prediction depends on assumptions that must be checked in the complete model and validation design.
Label a dataset's supplied hash-algorithm and salt-reference metadata.
When to use this tool
Use Data Row Hash Salt Label when you already have datasetCode, hashAlgorithm, saltReference and need to label a dataset's supplied hash-algorithm and salt-reference metadata.
How to use the tool and how it works
- Enter datasetCode, hashAlgorithm, saltReference. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read label. Use the output convention below.
Require datasetCode, hashAlgorithm and saltReference nonempty; format the labels.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| datasetCode | Dataset Code | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| hashAlgorithm | Hash Algorithm | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| saltReference | A supplied reference label, not a generated salt, secret value or anonymity guarantee. | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| label | Label | Required. Text; maximum UTF-16 length 8192. |
{
"input": {
"datasetCode": "DS17",
"hashAlgorithm": "SHA-256",
"saltReference": "key-ref-A"
},
"output": {
"label": "DS17; hash=SHA-256; salt reference=key-ref-A"
}
}Limitations and assumptions
- This computes no hash, creates no salt and establishes no anonymization, cryptographic strength, access control or safe disclosure.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Alternative or Complementary methods
Review the full data lineage, preprocessing fit, split design and evaluation procedure for model conclusions. Estimate required statistics only within the intended training folds and verify actual availability; local supplied-value transformations cannot establish those properties by themselves.
References
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