Public-Sector Deployments
Deployment evidence
Public-sector PET deployments can be unusually visible, but they are still contested. Look for both implementation evidence and impact on data users.
Measured Production And Recurring Deployments
2020 U.S. Census Disclosure Avoidance System
| Field | Entry |
|---|---|
| Organization / project | U.S. Census Bureau 2020 Disclosure Avoidance System |
| Domain | Public sector / official statistics |
| Problem | Release detailed census data while protecting respondent confidentiality against reconstruction and reidentification risks. |
| PETs used | Differential privacy, disclosure avoidance system, post-processing |
| Deployment maturity | Production |
| Source quality | Primary / official plus independent analysis |
| What worked | The Census Bureau used the Disclosure Avoidance System for 2020 Census data products and published extensive documentation. |
| Challenges | The deployment created major utility, communication, and stakeholder-trust debates, especially for small geographies and detailed counts. |
| Lessons for builders | DP deployments need public budget decisions, user education, demonstration data, and a plan for utility disputes. Formal privacy does not remove policy tradeoffs. |
| Source | Census Bureau Decennial Census Disclosure Avoidance and 2020 Disclosure Avoidance FAQ |
(Evidence: Deployment-backed / literature-backed. Source quality: Primary / official plus independent analysis. Reviewed 2026-06-17 — production use is clear, and public criticism is part of the evidence base.)
Boston wage-gap analysis using MPC
| Field | Entry |
|---|---|
| Organization / project | Boston Women's Workforce Council and Boston University Hariri Institute |
| Domain | Public sector / civic labor analytics |
| Problem | Measure gender and racial wage gaps across employers while protecting employer-submitted wage data. |
| PETs used | Secure multiparty computation |
| Deployment maturity | Production, batch / periodic civic analytics workflow |
| Source quality | Primary / official plus third-party case study |
| What worked | BWWC describes using MPC in its wage-gap analysis process and reports aggregated findings. |
| Challenges | MPC protects submitted inputs for the computation, but the released aggregate analysis still needs careful interpretation and cohort controls. |
| Lessons for builders | Civic analytics can use MPC for trust-building, but reporting design and participant communication remain central. |
| Source | Boston Women's Workforce Council: Data Privacy |
(Evidence: Deployment-backed. Source quality: Primary / official plus UN case study. Reviewed 2026-06-17 — recurring batch deployment evidence is unusually strong for civic MPC; output privacy remains a separate question.)
Common Proposed Use Cases
| Use case | Candidate PETs | Why proposed | Caveats |
|---|---|---|---|
| Official statistics releases | DP, synthetic data | Agencies must publish useful data under confidentiality mandates | Utility loss and public communication are hard |
| Cross-agency benefit analytics | MPC, clean rooms, TEEs | Agencies need joint analysis across legal boundaries | Governance and purpose limitation may dominate |
| Public-health dashboards | Federated analytics, DP | Local data can stay with health departments | Small geographies and rare conditions leak |
| Digital identity and eligibility checks | PSI, verifiable credentials, MPC | Agencies need yes/no checks without broad data sharing | The match result can still be sensitive |
Lessons Learned
- Public deployments need legitimacy, not only technical correctness.
- Formal privacy parameters become policy choices when outputs affect funding, representation, or rights.
- Demonstration data and stakeholder review are part of deployment, not afterthoughts.
- PETs can enable civic collaboration, but they cannot settle acceptable-use disputes by themselves.