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Securing your experience...
Securing your experience...
Add mathematically-proven privacy guarantees to aggregated analytics and reporting without sacrificing data utility.
You publish aggregate reports and statistics: 'US unemployment is 3.5%', 'COVID cases are down 20%', 'Average spend per customer is $500'. Attackers can use these aggregates to infer private data about individuals. Differential privacy adds guarantee that no individual can be identified or reverse-engineered.
We design and implement differential privacy deployments calibrated to your data sensitivity and reporting precision. We add calibrated noise to aggregates so that private data is mathematically unrecoverable, while aggregate statistics remain useful.
Privacy requirement gathering: understand which data entities need protection
Sensitivity analysis: compute how much each query could leak individual data
Noise calibration: determine minimal noise that satisfies privacy requirements
Utility validation: ensure aggregate statistics remain accurate and useful
Composition analysis: track privacy budgets across multiple queries
Differential privacy provides epsilon-delta bounds: even with all other data in the world, an attacker cannot determine if any specific individual is in your dataset. Privacy is mathematically proven, not just hoped for.
Track epsilon and delta across all queries. Ensure you don't exceed privacy requirements. Composition bounds are computed automatically.
Add minimal noise necessary to achieve privacy. Statistics remain accurate and actionable.
Works with your existing data pipeline. Can be applied at aggregation layer, database layer, or application layer.
Differential privacy is recognized by NIST and privacy regulators. Mathematical proof satisfies compliance audits.