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Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms

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arxiv 1809.07750 v3 pith:JEXLC5NQ submitted 2018-09-20 cs.CR

Noah Johnson , Joseph P. Near , Joseph M. Hellerstein , Dawn Song This is my paper · ORCID
classification cs.CR
keywords privacychorusdifferentialmechanismsscalablebuildingframeworkmechanism
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Differential privacy is fast becoming the gold standard in enabling statistical analysis of data while protecting the privacy of individuals. However, practical use of differential privacy still lags behind research progress because research prototypes cannot satisfy the scalability requirements of production deployments. To address this challenge, we present Chorus, a framework for building scalable differential privacy mechanisms which is based on cooperation between the mechanism itself and a high-performance production database management system (DBMS). We demonstrate the use of Chorus to build the first highly scalable implementations of complex mechanisms like Weighted PINQ, MWEM, and the matrix mechanism. We report on our experience deploying Chorus at Uber, and evaluate its scalability on real-world queries.

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  1. Composition for Pufferfish Privacy

    cs.CR 2026-02 conditional novelty 7.0 of 10

    Pufferfish mechanisms compose linearly only under DP-style inequalities; any per-entry DP mechanism can be translated to a composable Pufferfish mechanism using the a(b)-influence curve.

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