detect_sensitive_columns

Uses a broad, configurable regex library to match likely PII columns.
You can extend it with <code>extra_patterns</code> (they get ORed in) or replace
everything with a single <code>override_regex</code>.

Generate privacy-preserving synthetic datasets that mirror structure, types, factor levels, and missingness; export bundles for 'LLM' workflows (data plus 'JSON' schema and guidance); and build fake data directly from 'SQL' database tables without reading real rows. Methods are related to approaches in Nowok, Raab and Dibben (2016) <doi:10.32614/RJ-2016-019> and the foundation-model overview by Bommasani et al. (2021) <doi:10.48550/arXiv.2108.07258>.

Zobaer Ahmed

FakeDataR

Privacy-Preserving Synthetic Data for 'LLM' Workflows

detect_sensitive_columns function

<dl><dt>x_names</dt>
<dd>Character vector of column names to check.</dd>
<dt>extra_patterns</dt>
<dd>Character vector of additional regexes to OR in.
Examples: c("MRN", "NHS", "Aadhaar", "passport")</dd>
<dt>override_regex</dt>
<dd>Optional single regex string that fully replaces the
defaults (case-insensitive). When supplied, <code>extra_patterns</code> is ignored.</dd></dl>

Arguments

Detect sensitive columns by name — detect_sensitive_columns

<dl>

<dt>x_names</dt>
<dd>Character vector of column names to check.</dd>


<dt>extra_patterns</dt>
<dd>Character vector of additional regexes to OR in.
Examples: c("MRN", "NHS", "Aadhaar", "passport")</dd>


<dt>override_regex</dt>
<dd>Optional single regex string that fully replaces the
defaults (case-insensitive). When supplied, <code>extra_patterns</code> is ignored.</dd>

</dl>

detect_sensitive_columns: Detect sensitive columns by name

Description

Usage

Value

Arguments

Examples