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Limina supports scanning Comma Separated Value (CSV) files for PII and creating de-identified or redacted copies. Limina’s supported entity types function across each file type, with localized variants of different PII (Personally Identifiable Information) entities, PHI (Protected Health Information) entities, and PCI (Payment Card Industry) entities being detected. Our Supported Languages and Supported Entity Types page provides a more detailed look.

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How CSV Files Are Processed

Similar to Excel files, CSV files are processed using the method described for Tabular Data in the Structured Data Guide. The output file retains its original format with labels in place of the detected PII.

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Constraints

Please consider writing a handler for your specific application using the Structured Data Guide to get around any of the constraints listed below.
  • The file processing routes are synchronous, meaning that large files over 10MB in size may take a long time to process.
  • The data in the CSV file must be row-oriented (i.e. each row represents a separate record) and the headers must be on the first row.
  • Files must adhere to the csv file standards.

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Additional Options

CSV file processing is additionally supported by a specialized pre-processing parameter called csv_options. This parameter enables a sampling mode that provides efficiencies in overall file processing performance. The sampling feature looks at a selection of cells within each column of the file, predicts the entity type, and redacts the entirety of the column using those detected entity types. This can have a drastic improvement in performance when working with very large CSV files.

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Support Matrix

CPU ContainerGPU ContainerCommunity APIProfessional API
SupportedYesYesUp to 250 KiBNo

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Sample Request

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{
  "file": {
    "data": "<file_content_base64>",
    "content_type": "text/csv"
  },
  "entity_detection": {
    "return_entity": true
  }
}

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Sample Response

Response
{
  "processed_file": "Base64 Encoded File Content of the Redacted File",
  "processed_text": "string",
  "entities": "List[Entity]",
  "entities_present": true,
  "languages_detected": {"lang_1": 0.67, "lang_2": 0.74}
}