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- W3159706164 abstract "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." @default.
- W3159706164 created "2021-05-10" @default.
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- W3159706164 date "2020-09-01" @default.
- W3159706164 modified "2023-09-29" @default.
- W3159706164 title "Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms" @default.
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- W3159706164 doi "https://doi.org/10.1109/eurosp48549.2020.00041" @default.
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