HN: SynthWorld – deterministic synthetic identity graphs with ground truth

Faker generates rows. SynthWorld generates connected identity worlds with adversarial evidence and an answer key.

SynthWorld creates deterministic, safely fictional populations for evaluating privacy, PII-extraction, entity-resolution, relationship-inference, and exposure-analysis systems. Selected benchmark families expose separately serialized product-safe observations; other artifacts are evaluator bundles that retain answer keys for scoring.

SynthWorld began as the ground-truth harness for Idcognito and is deliberately usable as an independent Apache-2.0 Python package. It is not an anonymisation tool and does not transform sensitive real-world data into a safe dataset.

Requirement SynthWorld approach Repeatable evaluation Seeded generation, canonical ordering, frozen fixtures, and checksums Connected identities Personas share planted family, colleague, classmate, neighbour, and social evidence Measurable ambiguity Adversarial identity records include common names, Unicode, twins, maiden names, aliases, and misspellings Controlled oracle exposure Connection and risk provide separate public corpora; extraction currently ships an annotated evaluator bundle Safe fixtures Reserved domains, fictional phones, example addresses, invalid identifiers, and recursive synthetic: true markers Honest scoring Versioned formulas and benchmark integrity metrics make every published claim reproducible A generated row can test whether a field accepts an email address. A SynthWorld benchmark can measure whether a system extracts that address from a document, links several conflicting records to the correct entity, infers only supported relationships, and assigns the expected exposure score.

Core identity world: seeded personas, identity attributes, and evidence-backed relationships. Exposure corpus: breach, broker, search, and social observations, including zero-exposure controls, search collisions, and broker reappearance. Exact-span extraction: an annotated evaluator corpus pairing product-safe page content with evaluator-only occurrence labels. A separately serialized public-only projection is tracked in #13 . Entity resolution: opaque records and adversarial cases with separate entity-membership truth. Relationship inference: public association evidence, reciprocal positive cases, and unilateral negative controls. Risk calibration: provider-neutral breach observations with separately checksummed score, band, and factor truth. The core-world, exposure-corpus, extraction-corpus, connection-benchmark, and risk-benchmark schemas are independently versioned 1.0.0 contracts. See DATA_DICTIONARY.md for field definitions and the strict public/oracle boundary. See GOLDEN_REVIEW.md for the frozen benchmark review record.

SynthWorld currently uses two packaging patterns:

The intended separated evaluation flow is:

product or model evaluator | | v v public observations ----------> system predictions | v separate answer key | v scored results Only corpus types and CLI commands explicitly described as public should be passed to product adapters. Do not pass the annotated extraction corpus into a product or model without first projecting only its page fields.

The distribution is published as idcognito-synthworld ; the import package and the CLI are both named synthworld , and the package ships typed ( py.typed ). Release notes live in CHANGELOG.md .

pip install idcognito-synthworld synthworld generate --seed 20260719 --persona-count 10 --output world.json The frozen golden benchmarks are also browsable as tables on Hugging Face , byte-identical to the artifacts shipped in this package.

Install uv , clone the repository, and run:

uv sync --locked --all-groups uv run synthworld generate --seed 20260719 --persona-count 10 --output world.json uv run synthworld metrics --seed 20260719 --persona-count 10 Useful corpus commands include:

uv run synthworld generate-corpus --seed 20260719 --persona-count 10 --output exposures.json uv run synthworld generate-extraction --seed 20260719 --persona-count 10 --output extraction.json uv run synthworld generate-public-connections --seed 20260719 --persona-count 10 --output connections.json uv run synthworld generate-risk-public --seed 20260719 --persona-count 10 --output risk.json See examples/ for a worked exact-span extraction evaluation and annotated sample output.

The generate-extraction , generate-connection-benchmark , and generate-risk-answer commands include or emit evaluator-only truth. Keep those artifacts outside product and demo data paths. Today, generate-public-connections and generate-risk-public emit the separately serialized product-safe observations.

SynthWorld is intended to remain a focused ground-truth identity layer rather than become a second general-purpose simulator. Planned work is organised as packs and adapters:

The phased plan, architecture boundaries, and tracking issues are documented in ROADMAP.md .

make ci runs formatting, linting, strict type checking, all tests with 100% branch coverage, benchmark metrics at 10- and 100-persona scales, package inspection, and an isolated-wheel smoke test. The same gates run on Python 3.12 and 3.14 in GitHub Actions; a separate workflow job scans the repository's full history for secrets.

make ci Generated JSON is safely fictional for fixtures, demos, tutorials, and evaluation when its synthetic markers remain intact. That safety property does not make every artifact oracle-free product input; use only explicitly public corpora for product adapters. SynthWorld is not a source of real identity data and must never be used to impersonate, target, or investigate a person. Do not replace the safeguards with plausible real-world identifiers.

Copyright 2026 Redoubt Labs ltd. Licensed under the Apache License 2.0 .