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- W4288804735 abstract "Despite being raised as a problem over ten years ago, the imprecision of floating point arithmetic continues to cause privacy failures in the implementations of differentially private noise mechanisms. In this paper, we highlight a new class of vulnerabilities, which we call emph{precision-based attacks}, and which affect several open source libraries. To address this vulnerability and implement differentially private mechanisms on floating-point space in a safe way, we propose a novel technique, called emph{interval refining}. This technique has minimal error, provable privacy, and broad applicability. We use interval refining to design and implement a variant of the Laplace mechanism that is equivalent to sampling from the Laplace distribution and rounding to a float. We report on the performance of this approach, and discuss how interval refining can be used to implement other mechanisms safely, including the Gaussian mechanism and the exponential mechanism." @default.
- W4288804735 created "2022-07-30" @default.
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- W4288804735 date "2022-07-27" @default.
- W4288804735 modified "2023-09-26" @default.
- W4288804735 title "Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computers" @default.
- W4288804735 doi "https://doi.org/10.48550/arxiv.2207.13793" @default.
- W4288804735 hasPublicationYear "2022" @default.
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