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Difficulty: MediumData Governance, Classification, and Privacy Controls

A connected vehicle enterprise is updating its privacy engineering controls across its telemetry ingestion pipeline, billing service, and customer support portal. Match each privacy-enhancing technology on the left with its correct operational implementation on the right.

  • PseudonymizationReplaces vehicle driver identifiers in telemetry logs with cryptographic aliases using a key stored in a separate secure key-vault system, allowing controlled re-identification.
  • TokenizationExchanges sensitive primary account numbers (PAN) in payment processing systems with random surrogate strings generated by a centralized secure token vault.
  • Dynamic Data MaskingObfuscates sensitive customer account fields on screen for customer service agents in real time based on role-based permissions without modifying the underlying database record.
  • AnonymizationPermanently strips all direct and indirect personal identifying attributes from location history logs so that the resulting dataset cannot be re-identified by any party.

Answer

Pseudonymization matches with replacing identifiers using separate secure keys for reversible correlation; Tokenization matches with exchanging payment card data for surrogate vault tokens; Dynamic Data Masking matches with real-time on-screen obfuscation based on user roles; and Anonymization matches with permanently stripping identifiers to prevent re-identification.
Each technology corresponds to a distinct privacy mechanism: Pseudonymization replaces direct identifiers with reversible keys stored in a separate system; Tokenization substitutes sensitive data with non-sensitive surrogate tokens using a secure vault; Dynamic Data Masking alters displayed data at runtime for unauthorized roles while preserving the underlying storage; and Anonymization permanently removes all identifiable traits so re-identification is impossible.

Step-by-Step Solution

1
Analyze Pseudonymization requirements.
Identified that pseudonymization keeps data linked via a separate key, allowing reversible identification under strict access controls.
Pseudonymization protects privacy while maintaining data utility for authorized analysis.
2
Evaluate Tokenization characteristics.
Matched tokenization with substituting sensitive financial identifiers with random non-sensitive tokens managed via a vault.
Tokenization removes credit card numbers from application storage and downstream logging environments.
3
Differentiate Dynamic Data Masking from storage encryption.
Matched masking with real-time presentation obfuscation without altering underlying database values.
Masking enforces role-based view restrictions for personnel who do not need full data access.
4
Verify Anonymization irreversibility.
Matched anonymization with irreversible removal of identifying links across datasets.
Anonymized data is no longer subject to regulatory privacy constraints once re-identification is rendered impossible.

Key Concept

Privacy-Enhancing Technologies and Technical Privacy Controls
Estimated Time:1m 30s
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Topics

Data Governance, Classification, and Privacy Controls

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