Provable, not promised
Formal ε-budgets backed by mathematical proof. "Differentially private" means something specific here — not a marketing word, but a measurable property of the algorithm.
Differential privacy for PII analytics
Philter Diffuse
Philter Diffuse applies differential privacy to PII counts and aggregations, so you can answer questions like "how many SSNs flowed through this pipeline last month" without exposing individuals. Statistical utility without identification risk — backed by math, not best effort.
Why differential privacy
Membership-inference resistant
Adding or removing one record from the input changes the output by a bounded amount. An adversary with the result can't tell whether a specific individual was in the dataset.
Aggregate queries only
Counts, sums, averages — never raw records. The output preserves the population-level signal you need for analytics while making it impossible to reconstruct any single contributor.
Tunable budget
Lower ε means more noise and stronger privacy; higher ε means less noise and stronger utility. Pick the trade-off that fits your regulatory posture, per query.
Drop-in for Philter telemetry
Point Philter Diffuse at your Philter or Phield logs to run safe analytics on your own redaction telemetry — measure pipeline behavior without re-identifying the people the data was supposed to protect.
Open source
Every line of the noise calibration, sensitivity analysis, and budget accounting is inspectable. The math is auditable, the implementation is auditable, the privacy guarantee is reproducible.
Ready to use Philter Diffuse?
Three ways to get going — deploy the open source yourself, spin it up from a cloud marketplace, or work with our team directly. Pick the path that fits.