Private AI 4.0alpha – Checksum

Introducing Private AI’s Checksum Validation: Accurate Detection of Sensitive Data

At Private AI, ensuring privacy and data protection is at the heart of what we do. Our new Checksum Validation feature in the 4.0alpha release enhances the accurate identification of Credit Card Numbers (PCI) within your data. This ensures that sensitive information is correctly detected and can be securely managed or removed.

Why Checksum Validation Matters​

Detecting and protecting sensitive data like Credit Card Numbers is crucial for maintaining privacy and complying with data protection regulations. Traditional methods may miss these numbers, especially when they appear without context or in mislabeled structured data.

Checksum Validation addresses this challenge by:

  • • Accurate Identification: Utilizes methods like the Luhn check to confirm that entities detected as credit card numbers are valid.
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  • • Context-Independent Detection: Effectively identifies valid credit card numbers even when they lack surrounding context or are embedded in incorrectly labelled data fields.
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  • • Enhanced Privacy Protection: Allows for efficient detection and management of sensitive data, reducing the risk of accidental exposure and supporting your organization’s privacy commitments.

Ideal for Privacy-Focused Workflows

Whether you’re handling structured data, processing large datasets, or auditing for compliance, Checksum Validation ensures that sensitive information is accurately identified and appropriately managed or removed. This feature helps you:

  • • Strengthen Data Privacy: Ensure that credit card numbers are properly detected and protected within your data.
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  • • Maintain Compliance: Support adherence to data protection regulations by accurately identifying and handling sensitive information.
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  • • Improve Data Governance: Gain better control over your data assets by knowing exactly where sensitive information resides in your systems.

How Checksum Validation Works

• Validates Detected Numbers: When sequences resembling credit card numbers are found, Checksum Validation uses algorithms like the Luhn check to verify their validity.

• Seamless Integration: Easily incorporate Checksum Validation into your existing privacy workflows without disrupting operations.

Benefits of Using Checksum Validation

Reliable Detection

Achieve higher accuracy in identifying valid credit card numbers within your data.

Enhanced Privacy Protection

Proactively manage and secure sensitive data, reinforcing your commitment to privacy and data security.

Supports Compliance Efforts

Facilitate adherence to data protection laws and standards like PCI DSS by ensuring sensitive information is properly identified and handled.

Ready to strengthen your data protection strategy?

With Checksum Validation, you have a powerful tool to bolster your data protection efforts. By accurately identifying and validating sensitive numbers, you can confidently safeguard personal information and uphold data privacy standards.

Enhance your privacy safeguards today with Private AI’s latest innovation.

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Language Packs

Expand the categories below to see which languages are included within each language pack.
Note: English capabilities are automatically included within the Enterprise pricing tier. 

French
Spanish
Portuguese

Arabic
Hebrew
Persian (Farsi)
Swahili

French
German
Italian
Portuguese
Russian
Spanish
Ukrainian
Belarusian
Bulgarian
Catalan
Croatian
Czech
Danish
Dutch
Estonian
Finnish
Greek
Hungarian
Icelandic
Latvian
Lithuanian
Luxembourgish
Polish
Romanian
Slovak
Slovenian
Swedish
Turkish

Hindi
Korean
Tagalog
Bengali
Burmese
Indonesian
Khmer
Japanese
Malay
Moldovan
Norwegian (Bokmål)
Punjabi
Tamil
Thai
Vietnamese
Mandarin (simplified)

Arabic
Belarusian
Bengali
Bulgarian
Burmese
Catalan
Croatian
Czech
Danish
Dutch
Estonian
Finnish
French
German
Greek
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Italian
Japanese
Khmer
Korean
Latvian
Lithuanian
Luxembourgish
Malay
Mandarin (simplified)
Moldovan
Norwegian (Bokmål)
Persian (Farsi)
Polish
Portuguese
Punjabi
Romanian
Russian
Slovak
Slovenian
Spanish
Swahili
Swedish
Tagalog
Tamil
Thai
Turkish
Ukrainian
Vietnamese

Rappel

Testé sur un ensemble de données composé de données conversationnelles désordonnées contenant des informations de santé sensibles. Téléchargez notre livre blanc pour plus de détails, ainsi que nos performances en termes d’exactitude et de score F1, ou contactez-nous pour obtenir une copie du code d’évaluation.

99.5%+ Accuracy

Number quoted is the number of PII words missed as a fraction of total number of words. Computed on a 268 thousand word internal test dataset, comprising data from over 50 different sources, including web scrapes, emails and ASR transcripts.

Please contact us for a copy of the code used to compute these metrics, try it yourself here, or download our whitepaper.