Anonymization Techniques Reference

Anonymization Techniques Reference#

Reference for data anonymization methods: k-anonymity, l-diversity, t-closeness, differential privacy, and Zipminator’s 10-level system.

Why Anonymization Matters#

Post-quantum encryption protects data in transit and at rest. Anonymization protects data in use: when datasets are shared for analysis, research, or compliance, the original identities must not be recoverable.

Technique Overview#

Technique	What It Does	Strength	Weakness
k-Anonymity	Each record is indistinguishable from \(k-1\) others	Simple, well-understood	Vulnerable to homogeneity/background knowledge attacks
l-Diversity	Each equivalence class has \(l\) distinct sensitive values	Protects against homogeneity	Does not consider value distribution
t-Closeness	Distribution of sensitive values in each class is within \(t\) of the overall distribution	Strong distributional privacy	Computationally expensive
Differential Privacy	Adding calibrated noise ensures individual records cannot be distinguished	Formal mathematical guarantee (\(\epsilon\)-DP)	Reduces data utility
Synthetic Data	Generate new data with same statistical properties	Preserves utility, no real records	Quality depends on generation model

k-Anonymity#

A dataset satisfies \(k\)-anonymity if every combination of quasi-identifiers (age, zip code, gender) appears at least \(k\) times.

Techniques to achieve k-anonymity:

Generalization: Replace specific values with ranges (age 28 \(\rightarrow\) 25-30)
Suppression: Remove rare records or values
Cell suppression: Replace individual cells with “*”

l-Diversity#

Extends k-anonymity by requiring that each equivalence class has at least \(l\) distinct values for the sensitive attribute.

t-Closeness#

Extends l-diversity by requiring that the distribution of sensitive values in each equivalence class is within distance \(t\) of the overall distribution, measured by Earth Mover’s Distance.

Differential Privacy#

A randomized mechanism \(\mathcal{M}\) satisfies \(\epsilon\)-differential privacy if for any two datasets \(D\) and \(D'\) differing in one record:

\[P[\mathcal{M}(D) \in S] \leq e^\epsilon \cdot P[\mathcal{M}(D') \in S]\]

The parameter \(\epsilon\) (privacy budget) controls the privacy-utility trade-off:

\(\epsilon = 0.1\): Strong privacy, significant noise
\(\epsilon = 1.0\): Moderate privacy, usable for most analyses
\(\epsilon = 10\): Weak privacy, minimal noise

Common mechanisms:

Laplace mechanism: Add \(\text{Lap}(\Delta f / \epsilon)\) noise to numeric queries
Gaussian mechanism: Add \(\mathcal{N}(0, \sigma^2)\) noise with \((\epsilon, \delta)\)-DP guarantee
Exponential mechanism: Sample from a distribution proportional to utility score

Zipminator’s 10-Level Anonymization System#

Zipminator provides a configurable anonymization pipeline with 10 progressive levels:

Level	Name	What It Does
L0	Raw	No anonymization (original data)
L1	Pseudonymization	Replace names/IDs with random tokens
L2	Generalization	Generalize quasi-identifiers (age ranges, zip prefixes)
L3	k-Anonymity (k=5)	Ensure 5-anonymity on quasi-identifiers
L4	k-Anonymity (k=10)	Stricter anonymity threshold
L5	l-Diversity (l=3)	Add sensitive attribute diversity
L6	t-Closeness (t=0.2)	Distributional privacy guarantee
L7	Differential Privacy (\(\epsilon=1.0\))	Formal DP guarantee, moderate noise
L8	Differential Privacy (\(\epsilon=0.1\))	Strong DP guarantee, more noise
L9	Synthetic	Replace with synthetic data preserving statistical properties

Levels L0-L3 are available on the Free tier. L4+ requires a Developer or higher subscription.

PII Detection#

Before anonymization, Zipminator scans for personally identifiable information across 15 countries:

Region	PII Patterns Detected
US	SSN, driver’s license, passport
UK	National Insurance, NHS number
NO	F-nummer, D-nummer, bank account
SE	Personnummer, samordningsnummer
DK	CPR-nummer
FI	HETU
DE	Personalausweis, Steuer-ID
FR	NIR, carte d’identite
EU	IBAN, VAT number, EHIC
IN	Aadhaar, PAN
BR	CPF, CNPJ
JP	My Number, driver’s license
CA	SIN, driver’s license
AU	TFN, Medicare
AE	Emirates ID