Personal names represent one of the most fundamental forms of identification, appearing in virtually every document containing personal information. Yet names also present one of the most challenging detection problems in data protection. Unlike structured identifiers like Social Security Numbers that follow fixed formats, names exhibit extraordinary variation across cultures, languages, and contexts.
Effective name detection requires understanding that names are not merely character patterns but cultural artifacts embedded in social context. A name that's common in one culture may be unknown in another. Naming patterns vary significantly—given name first in Western cultures, family name first in East Asian cultures, patronymic elements in Arabic cultures, multiple surnames in Hispanic cultures. Accurate detection must navigate this complexity.
Cultural Naming Patterns
Our AI models understand the rich diversity of naming conventions worldwide:
Western Names: Typically follow given name + middle name(s) + surname pattern. May include titles (Dr., Mr., Ms.), suffixes (Jr., III, PhD), and compound surnames (Smith-Jones).
East Asian Names: Chinese, Japanese, and Korean names typically place family name first. Chinese names usually have one or two character given names. Japanese names may be written in kanji, hiragana, or katakana with different detection needs.
Arabic Names: Often include nasab (patronymic, ibn/bin meaning "son of"), laqab (nickname or descriptive), and nisba (geographical origin). Full formal names can be quite long.
Hispanic Names: Traditionally include both paternal and maternal surnames (García López), with the paternal surname typically listed first. Compound given names are common (Juan Carlos).
Contextual Disambiguation
Names share patterns with many non-name entities. "Amazon" could be a company, a river, or a person. "Washington" could be a city, state, or person. "Apple" could be a company or... well, an apple. Contextual AI analyzes surrounding text to make accurate classifications.
Key disambiguation signals include: personal pronouns (he, she, they), titles and honorifics (Mr., Dr., President), relationship terms (father, colleague, patient), action verbs associated with people, and document context (medical record implies person names, not place names).
Handling Name Variations
The same person may be referenced by many name forms within a single document or across documents. Full formal name, first name only, nickname, initials, surname only—all may refer to the same individual. Comprehensive protection requires recognizing these variations.
Our system maintains mappings between formal names and common variations. When a full name is detected, subsequent references that could be variations (same first name, matching surname, known nicknames) are flagged for consistent treatment.
Regulatory Requirements for Names
Names are protected under virtually every privacy regulation. HIPAA lists names as the first of 18 identifiers requiring removal for de-identification. GDPR defines names as personal data by default. FOIA's privacy exemption commonly applies to names of private citizens. Consistent, accurate name detection is foundational to privacy compliance.
