google/differential-privacy

Health v2 · maintenance only

Adoption not part of the score

3346 stars · 430 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Google's collection of differential privacy libraries for computing ε- and (ε, δ)-differentially private statistics over datasets. It includes end-to-end frameworks (Privacy on Beam for Go, PipelineDP4j for JVM), low-level noise/aggregation building blocks in C++, Go, and Java, a privacy budget accounting library, a DP auditing tool, and a CLI for differentially private SQL queries.

Use cases

• compute differentially private statistics over a dataset
• add calibrated noise to aggregations to protect user privacy
• track and account for privacy budget (epsilon) in a data pipeline
• run differentially private SQL queries
• audit whether a system's differential privacy guarantees hold
• build anonymized analytics on Apache Beam or Spark pipelines

When to choose

• you need production-grade differential privacy in Go, Java/Kotlin/Scala, C++, or Python
• you want an easy end-to-end DP framework on top of Beam or Spark
• you need low-level DP primitives like noise addition and private aggregations
• you must track privacy budget or audit DP guarantees

When to avoid

• you need simple anonymization or pseudonymization rather than formal DP guarantees
• you need DP support in a language not covered (e.g., Ruby, PHP)
• you rely on the experimental tools (SQL CLI, auditor) needing API stability
• your team has no familiarity with differential privacy concepts and cannot invest in learning them

Facets

library · maturity active

privacy security data-science testing cli privacy data-science analytics security developer-tools go cpp jvm python cross-platform differential-privacy anonymization apache-beam apache-spark privacy-budget-accounting sql statistics

Member repositories

For agents

markdown · JSON · MCP: product_card(name="google/differential-privacy")