privacytrustlab/ml_privacy_meter

• highreadme#1

  Reposition the README's opening to clarify its unique auditing focus

  Why:

  CURRENT

  Privacy Meter is an open-source library to audit data privacy in a wide range of statistical and machine learning algorithms (classification, regression, computer vision, and natural language processing). The tool enables data protection impact assessment based on the state-of-the-art membership inference attacks.

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  Privacy Meter is an open-source library for **quantitatively auditing data privacy risks** in machine learning models. It specializes in **assessing information leakage** through state-of-the-art membership inference attacks, enabling **data protection impact assessment** across various ML algorithms (classification, regression, computer vision, NLP). Unlike differential privacy libraries or fairness tools, Privacy Meter provides the metrics and tools to *measure* and *evaluate* the privacy posture of your deployed models.

  OpenDoneDismiss
• mediumhomepage#2

  Add a homepage URL to the repository's 'About' section

  Why:

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  https://github.com/privacytrustlab/ml_privacy_meter

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• mediumtopics#3

  Refine repository topics for sharper focus on privacy auditing

  Why:

  CURRENT

  data-privacy, data-protection, data-protection-impact-assessment, explainable-ai, gdpr, inference, information-leakage, machine-learning, membership-inference-attack, privacy, privacy-audit

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  data-privacy, data-protection, data-protection-impact-assessment, gdpr, inference, information-leakage, machine-learning, membership-inference-attack, privacy, privacy-audit, privacy-risk-assessment, ml-privacy

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to quantitatively assess data privacy risks in deployed machine learning models?

  you: not recommended
  AI recommended (in order):

  1. IBM AI Fairness 360 (AIF360)
  2. Google's Differential Privacy Library (TensorFlow Privacy / JAX Privacy)
  3. Microsoft's SmartNoise (OpenDP)
  4. ARX Data Anonymization Tool
  5. Pytorch Opacus
  6. CleverHans
  7. PySyft
  8. TenSEAL

  AI recommended 8 alternatives but never named privacytrustlab/ml_privacy_meter. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Tool for evaluating information leakage and data protection impact in AI systems?

  you: not recommended
  AI recommended (in order):

  1. IBM AI Explainability 360 (AIX360) (IBM/AIX360)
  2. Google Cloud Data Loss Prevention (DLP) API
  3. Microsoft Azure Confidential Computing
  4. OpenMined PySyft (OpenMined/PySyft)
  5. Fiddler AI Observability Platform
  6. Privitar Nova
  7. OWASP AI Exchange (AIX) (OWASP/AI-Exchange)

  AI recommended 7 alternatives but never named privacytrustlab/ml_privacy_meter. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of privacytrustlab/ml_privacy_meter?

  pass
  ✓AI named privacytrustlab/ml_privacy_meter explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts privacytrustlab/ml_privacy_meter in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named privacytrustlab/ml_privacy_meter explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo privacytrustlab/ml_privacy_meter solve, and who is the primary audience?

  pass
  ✓AI named privacytrustlab/ml_privacy_meter explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?