Synthetic Person Profile — Large-Scale Fidelity Report

An independent quality-assurance review of a single large snapshot of synthetic person-profile records, generated end-to-end as one job. Tests uniqueness, fidelity to public reference distributions, and internal consistency at 2,000,000-record scale.

Executive summary

1. Dataset characteristics

Uniqueness with 95% Wilson confidence intervals

Identity-tuple collisions are structurally expected at scale — see the birthday-paradox section below for the analytical derivation. Whole-row and ID-field collisions are the diagnostic uniqueness signals.

2. Distribution fidelity

For each marginal distribution where a published US-adult reference exists, the observed proportions are compared against the reference using a formal statistical test. A MATCH indicates the null hypothesis (observed and reference are drawn from the same distribution) is not rejected at α = 0.05.

Categorical attributes (chi-squared goodness-of-fit)

Numeric attributes (two-sample Kolmogorov–Smirnov)

Observed vs reference — histograms

Bars show the observed empirical density; the red line is the parametric reference density at the same support.

3. Joint structure

3a. Diabetes prevalence by age band

3b. Mean BMI by age band

3c. Inter-attribute Pearson correlations

Practical-equivalence band ±0.15. Sign mismatches reported separately.

4. Uniqueness & birthday-paradox analysis

The identity-tuple uniqueness percentage decreases at scale because the (given_name × family_name × date_of_birth) value space is finite. The expected collision count is computed and compared against the observed.

5. Format validity

6. Quality assurance summary

All cross-field invariant checks pass on this snapshot. 2,000,000 records, 0 violations.

The check battery covers: BMI consistency with height & weight; ZIP code consistency with state; pregnancy_status consistency with sex_at_birth; insulin use consistency with diabetes_status; prescription-count consistency with chronic-medication flags; age within the adult range; and ~30 other invariants.

7. Limitations

• Identity-tuple collisions are structurally expected at scale. At n = 2,000,000 records drawn from a finite name × DOB pool of size ~51,246,000, the expected number of (given_name, family_name, date_of_birth) collisions is approximately 39027.4; the observed count of 46,166 matches the birthday-paradox prediction (AS EXPECTED). This is a property of finite name pools, not a generator defect. Use the ID field for guaranteed uniqueness.
• en-GB and en-IN locales use en-US health reference data as fallback, disclosed via a per-record metadata field. en-US is the only locale with native health-reference calibration in this engine version.
• Cohort-level fidelity is out of scope for this report. Longitudinal events (encounters, prescriptions filled, lab observations over time) are produced by separate engines and reported separately.
• This is engine v0.4 — early in a versioned roadmap. Each release narrows specific gaps; this report is the public quality ledger for one snapshot generated by one engine version. The published version stamp on every record makes future reports trivially comparable.

8. Privacy & re-identification

By construction, every value in this dataset is drawn from a synthetic distribution. No record corresponds to a real person; no field is derived from any real record. As a consequence:

• The dataset is HIPAA-equivalent on its face: there are no protected health identifiers because there are no real-world identifiers at all. (Formal HIPAA Safe Harbor attestation by a statistician is a separate compliance artifact; this report makes the structural argument.)
• Re-identification via attribute combination (the "linkage attack" threat against anonymized real data) does not apply. There is no underlying real distribution whose tails could uniquely identify an individual.
• Identity-tuple collisions (multiple records sharing first_name + family_name + date_of_birth) are an expected statistical artifact of drawing from finite name pools, not a privacy concern. See the birthday-paradox derivation above.
• Sensitive-format fields (ssn_last_four, employer_ein) use structurally invalid prefixes that cannot collide with real-world assignments, so a test ID never accidentally resembles a real one.

9. Reproducibility

Datasets of this size can be requested via the asynchronous endpoint:

POST /v1/datasets/person
{
  "count":   2000000,
  "seed":    <any uint64>,
  "locale":  "en-US",
  "preset":  "us_2024_adults"
}

The endpoint returns a job ID; poll for completion and retrieve a download link from the status endpoint. Each request with the same seed produces the same dataset at the engine version listed in the header.