Every Query Counts: Analyzing the Privacy Loss of Exploratory Data Analyses

Authors: Saskia Nuñez von Voigt, Mira Pauli, Johanna Reichert, Florian Tschorsch
Published: 2020-08-27

Source: https://arxiv.org/abs/2008.12282

PDF: https://arxiv.org/pdf/2008.12282

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プライバシー損失分析 プライバシー評価 データ生成

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Abstract

An exploratory data analysis is an essential step for every data analyst to gain insights, evaluate data quality and (if required) select a machine learning model for further processing. While privacy-preserving machine learning is on the rise, more often than not this initial analysis is not counted towards the privacy budget. In this paper, we quantify the privacy loss for basic statistical functions and highlight the importance of taking it into account when calculating the privacy-loss budget of a machine learning approach.