testland/data-masking-techniques-reference
Pure-reference catalog of data-masking techniques and de-identification privacy models. Enumerates the seven canonical masking operators (substitution, shuffling, number/date variance, encryption, hashing, nulling, masking-out / character-scrambling) plus tokenisation, redaction, format-preserving encryption, and Microsoft Presidio's six built-in operators. Distinguishes reversible techniques (pseudonymisation candidates per GDPR Art. 4(5)) from irreversible techniques (anonymisation candidates), and maps them to NIST SP 800-188 privacy models - k-anonymity, l-diversity, t-closeness, differential privacy (deep model definitions in references/). Cites ISO/IEC 20889:2018 for the standard taxonomy. Use to pick the right masking operator per field type and risk level.
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privacy-models.mdreferences/
Privacy models - NIST SP 800-188
Deep reference for data-masking-techniques-reference SKILL.md. Consult
after per-field operators are chosen, when quasi-identifiers remain and the
whole dataset's disclosure risk must be bounded.
NIST SP 800-188:2023 ("De-Identifying Government Datasets", csrc.nist.gov/pubs/sp/800/188/final) formalises statistical privacy models layered above the per-field techniques.
k-anonymity
A dataset is k-anonymous if every record is indistinguishable from at least k - 1 other records when projected on the quasi-identifiers (Sweeney 2002, cited in NIST 800-188).
- Achieve via: Generalisation (age 47 → "40 - 50"), suppression (drop the row), and aggregation.
- Picks
k: Typical values are k = 5, k = 10, k = 100 depending on dataset size + risk tolerance. - Weakness: Vulnerable to homogeneity attack - if all k records share the same sensitive value, k-anonymity doesn't protect it.
l-diversity
Strengthens k-anonymity by requiring at least l well-represented values of the sensitive attribute within each equivalence class (Machanavajjhala et al. 2007).
- Achieve via: Suppression of records that would break l, or perturbation of sensitive values.
- Weakness: Vulnerable to skewness / similarity attack - the l values may be semantically similar.
t-closeness
Strengthens l-diversity by requiring the distribution of the sensitive attribute in each equivalence class be close (within t, by Earth Mover's Distance) to the distribution in the overall dataset (Li et al. 2007).
- Trade-off: Higher t = better utility, lower t = stronger privacy.
Differential privacy
A formal mathematical guarantee: the probability of any output changes by at most a multiplicative factor (e^ε) when a single record is added/removed. ε (epsilon) is the privacy budget - lower ε = stronger privacy.
- Achieve via: Noise injection (Laplace / Gaussian mechanism) on query outputs, not on the raw dataset.
- Trade-off: Utility-vs-budget. Apple, Google, US Census 2020 use differential privacy.
- Cite: NIST SP 800-188:2023 §6; original Dwork 2006 "Calibrating noise to sensitivity."
Picking the model
- k-anonymity - baseline for quasi-identifier sets; pick k >= 5 (k = 10+ for high-risk datasets).
- l-diversity - add when a homogeneity attack on the sensitive attribute is plausible.
- t-closeness - add when the sensitive attribute's distribution itself leaks (skewness / similarity).
- Differential privacy - use for statistical query release, not raw-dataset export; keep ε <= 1 for strong privacy, ε <= 10 relaxed.
References
- NIST SP 800-188:2023 "De-Identifying Government Datasets" - csrc.nist.gov/pubs/sp/800/188/final. Definitions of k-anonymity, l-diversity, t-closeness, differential privacy.
- Sweeney 2002 (k-anonymity), Machanavajjhala et al. 2007 (l-diversity), Li et al. 2007 (t-closeness), Dwork 2006 "Calibrating noise to sensitivity" (differential privacy) - all cited in NIST 800-188.