Data Anonymization & Privacy Engineering

We make your customer data safe to use, share and model — without exposing a single individual.

The problem

The data with the most value is the data with the most risk.

Raw customer records cannot be handed to an agency, a partner or a model without exposure, so teams either take the risk or leave the data unused.

What we do

  • Classify personal, sensitive and derived fields across your sources
  • Pseudonymize and hash identifiers, with key management kept separate from the data
  • Aggregate and generalize attributes where individual detail adds no value
  • Apply k-anonymity, differential privacy and noise where the use case requires it
  • Build clean-room-ready datasets that can be matched with partners without sharing raw records
  • Generate synthetic datasets for development and testing, so engineers never touch production records (to confirm)
  • Document retention, deletion and re-identification risk for your DPO

How it works

  1. Data classification and risk assessment against your legal requirements
  2. We choose the technique per use case: what must stay linkable, what can be aggregated away
  3. Implementation inside the pipeline, so anonymization happens before the data reaches any analyst or model
  4. Re-identification testing: we attack our own output before anyone else can

What you get

Anonymized datasets ready for analysis, modeling and partner matching, plus documentation your legal and compliance teams can use.

Where AI comes in

Models detect personal information hiding in unstructured fields, flag combinations of attributes that could re-identify someone, and generate synthetic records that keep the statistical shape of the original data without its individuals.

Related: First-Party Data Foundation · AI Audience Modeling

FAQ

Yes. Done properly, it keeps the patterns models need and removes the identities they do not.

It is a significant part of it. We build to GDPR and US state privacy requirements, and your legal counsel signs off the final assessment.

Yes, through hashed identifiers and clean rooms, without either side sharing raw data.

It stays in a controlled layer with restricted access; only the derived, anonymized layer is used downstream.

Talk to our team