Technology dataset

{
  "title": "ABIS & Deduplication",
  "slug": "abis-dedup",
  "summary": "Automated Biometric Identification Systems perform large-scale 1:N searches to resolve identities and detect duplicates.",
  "order": 23,
  "published": true,
  "also_known_as": [
    "ABIS",
    "deduplication",
    "1:N search"
  ],
  "category": "Technology",
  "tags": [
    "biometrics",
    "identity",
    "standards"
  ],
  "see_also": [
    "fingerprint-recognition",
    "facial-recognition",
    "iris-recognition"
  ],
  "standards": [
    {
      "label": "ANSI/NIST-ITL 1-2011 biometric data format",
      "url": "https://www.nist.gov/publications/ansi-nist-itl-1-2011-data-format"
    }
  ],
  "last_reviewed": "2025-11-10",
  "faq": [
    {
      "question": "What does an ABIS do?",
      "answer": "It searches biometric databases to identify individuals and flag duplicate enrollments across large populations."
    },
    {
      "question": "What’s the difference between identification and verification?",
      "answer": "Identification is 1:N (who is this?); verification is 1:1 (is this person who they claim?). ABIS primarily supports 1:N at scale."
    },
    {
      "question": "How are false positives managed?",
      "answer": "Systems tune thresholds and use adjudication workflows; quality checks and multi‑modal fusion reduce spurious hits."
    }
  ],
  "references": [
    {
      "label": "NIST biometric standards resources",
      "url": "https://www.nist.gov/itl/iad/image-group/biometric-standards"
    },
    {
      "label": "ANSI/NIST-ITL 1 data format",
      "url": "https://www.nist.gov/publications/ansi-nist-itl-1-2011-data-format"
    },
    {
      "label": "ISO/IEC 19795-1 performance testing",
      "url": "https://www.iso.org/standard/41446.html"
    }
  ],
  "createdAt": "2025-11-10T15:01:52.782Z",
  "updatedAt": "2025-11-10T16:03:39.373Z",
  "name": "ABIS & Deduplication",
  "license": "CC-BY-4.0",
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  "description": "## Overview\nABIS platforms store and search biometric templates at national or global scale. Deduplication ensures each person has only one identity record.\n\n## How it works\n1. Capture biometric samples during enrollment.  \n2. Extract features and store templates.  \n3. Run 1:N searches to detect matches or duplicates.  \n4. Adjudicate hits and maintain watchlists.\n\n## Common use cases\n- National ID enrollment\n- Border and visa vetting\n- Civil or criminal watchlists\n\n## Strengths and limitations\n**Strengths:** Scales to millions; prevents multiple identities.  \n**Limitations:** Infrastructure cost; privacy and governance.\n\n## Key terms\n- **1:N search:** Matching a probe against all records.  \n- **Deduplication:** Removing duplicate identities in a database."
}

{
  "title": "Age Assurance (Verification & Estimation)",
  "slug": "age-assurance",
  "summary": "Processes that verify age via trusted documents or estimate age from signals like face or behavior for online safety compliance.",
  "order": 18,
  "published": true,
  "also_known_as": [
    "age verification",
    "age estimation"
  ],
  "category": "Technology",
  "tags": [
    "privacy",
    "regulation",
    "standards"
  ],
  "see_also": [
    "facial-recognition",
    "digital-id"
  ],
  "standards": [
    {
      "label": "PAS 1296: Age verification",
      "url": "https://shop.bsigroup.com/products/pas-1296-age-verification"
    }
  ],
  "last_reviewed": "2025-11-10",
  "faq": [
    {
      "question": "How does age estimation differ from verification?",
      "answer": "Estimation predicts age from signals, while verification checks authoritative documents or records."
    },
    {
      "question": "What inputs are used for estimation?",
      "answer": "Commonly face images or short videos; some systems consider behavioral signals. Estimation should include bias and uncertainty reporting."
    },
    {
      "question": "How is privacy handled?",
      "answer": "Prefer on-device processing, minimize retention, and use privacy-preserving proofs where possible; follow regulator guidance (e.g., data minimization)."
    }
  ],
  "references": [
    {
      "label": "UK Ofcom guidance on age assurance",
      "url": "https://www.ofcom.org.uk/online-safety/age-assurance"
    },
    {
      "label": "ICO Age Appropriate Design Code",
      "url": "https://ico.org.uk/for-organisations/ico-codes-of-practice/age-appropriate-design-a-code-of-practice-for-online-services/"
    },
    {
      "label": "BSI PAS 1296: Age verification",
      "url": "https://shop.bsigroup.com/products/pas-1296-age-verification"
    }
  ],
  "createdAt": "2025-11-10T15:01:52.782Z",
  "updatedAt": "2025-11-10T15:59:34.990Z",
  "name": "Age Assurance (Verification & Estimation)",
  "license": "CC-BY-4.0",
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  "description": "## Overview\nAge assurance tools help platforms meet legal requirements by verifying or estimating user ages before granting access to restricted content or services.\n\n## How it works\n1. **Verification:** Validate a government ID or authoritative record.  \n2. **Estimation:** Use biometrics or behavior (e.g., face, voice, activity) to infer age.  \n3. **Decision:** Apply policy thresholds (e.g., over 18) and handle appeals or manual review.\n\n## Common use cases\n- Social media sign-up\n- Online gaming and gambling\n- Adult content gateways\n\n## Strengths and limitations\n**Strengths:** Supports regulatory compliance; multiple methods.  \n**Limitations:** Privacy concerns; estimation accuracy varies.\n\n## Key terms\n- **Age verification:** Checking an ID or database to confirm age.  \n- **Age estimation:** Predicting age from biometric or behavioral signals."
}

{
  "slug": "behavioral-biometrics",
  "name": "Behavioral Biometrics",
  "summary": "Behavioral biometrics analyzes patterns in user interactions—such as typing rhythm, mouse movement, or touchscreen gestures—to continuously verify identity.",
  "order": 10,
  "published": true,
  "references": [
    {
      "label": "NIST: Behavioral biometrics program overview",
      "url": "https://www.nist.gov/programs-projects/behavioral-biometrics"
    },
    {
      "label": "NIST SP 800-63B (AAL, user verification context)",
      "url": "https://pages.nist.gov/800-63-3/sp800-63b.html"
    }
  ],
  "createdAt": "2025-05-17T00:00:00.000Z",
  "updatedAt": "2025-11-10T00:00:00.000Z",
  "faq": [
    {
      "question": "What is behavioral biometrics?",
      "answer": "Behavioral biometrics analyzes patterns in user interactions, such as typing rhythm, mouse movements, or touchscreen gestures, to verify identity continuously."
    },
    {
      "question": "How does behavioral biometrics differ from physiological biometrics?",
      "answer": "Behavioral biometrics focuses on how users behave, whereas physiological biometrics rely on static physical traits like fingerprints or iris patterns."
    },
    {
      "question": "Where is behavioral biometrics commonly used?",
      "answer": "It is used for continuous authentication, fraud detection, and adaptive security in banking, healthcare, and enterprise environments."
    },
    {
      "question": "What about privacy and consent?",
      "answer": "Deployments should disclose passive monitoring and adhere to privacy laws; many systems process derived features rather than raw inputs to reduce risk."
    }
  ],
  "title": "Behavioral Biometrics",
  "category": "Technology",
  "license": "CC-BY-4.0",
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  "description": "Behavioral biometrics leverages how a user interacts with devices or interfaces rather than physical traits. By monitoring characteristics like keystroke dynamics, touchscreen swipes, or navigation habits, systems can provide continuous and passive authentication. This modality enhances security by detecting anomalies in real-time and can complement physiological biometrics for multifactor authentication."
}