A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
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DOI：
10.1145/3588701
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发表时间：
2022-08
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通讯作者：
Ritesh Ahuja;Sepanta Zeighami;G. Ghinita;C. Shahabi
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作者：
Ritesh Ahuja;Sepanta Zeighami;G. Ghinita;C. Shahabi
几个由大公司(如Meta，谷歌)发起的“数据向善”项目[1，5，12]向公众发布时空数据集，以利于新冠肺炎传播建模[17，47，]和了解人类的流动性[14，24]。大多数情况下，时空数据是以高分辨率人口密度信息的快照形式提供的，其中发布的统计数据记录了小区域的短时间内的人口计数。由于公用事业需要高分辨率(例如，在对COVID热点进行建模时)，隐私风险增加。为了防止恶意行为者使用这些数据来推断有关个人的敏感细节，必须首先对发布的数据集进行消毒。通常，[1，5，7，12]，差异隐私(DP)被用作保护模型，因为其正式的保护保证防止攻击者了解特定个人的数据是否已被包括在发布中。
Several "data-for-good" projects [1, 5, 12] initiated by major companies (e.g., Meta, Google) release to the public spatio-temporal datasets to benefit COVID-19 spread modeling [17, 47, 64] and understand human mobility [14, 24]. Most often, spatio-temporal data are provided in the form of snapshot high resolution population density information, where the released statistics capture population counts in small areas for short time periods. Since high resolution is required for utility (e.g., in modeling COVID hotspots) privacy risks are elevated. To prevent malicious actors from using the data to infer sensitive details about individuals, the released datasets must be first sanitized. Typically, [1, 5, 7, 12], differential privacy (DP) is employed as protection model, due to its formal protection guarantees that prevent an adversary to learn whether a particular individual's data has been included in the release or not.