数据在云中的地理位置

Mark A. Gondree, Zachary N. J. Peterson
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引用次数: 90

摘要

我们介绍并分析了一个通用框架,用于将数据真实地绑定到一个位置,同时提供强大的保证,防止云存储提供商(意外或恶意)试图重新定位云数据。然后,我们评估了该框架中的初步解决方案,该解决方案将基于约束的主机地理定位与数据拥有证明相结合,称为基于约束的数据地理定位(CBDG)。我们使用PlanetLab和真实云存储服务的实验组合来评估CBDG,证明我们可以以高精度将获取的数据绑定到最初托管数据的位置。我们将托管在大多数PlanetLab目标上的数据定位到不大于118,000 km^2的区域,并将托管在Amazon S3上的数据定位到不大于12,000 km^2的区域,该区域小到足以识别州或服务区域。
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Geolocation of data in the cloud
We introduce and analyze a general framework for authentically binding data to a location while providing strong assurances against cloud storage providers that (either accidentally or maliciously) attempt to re-locate cloud data. We then evaluate a preliminary solution in this framework that combines constraint-based host geolocation with proofs of data possession, called constraint-based data geolocation (CBDG). We evaluate CBDG using a combination of experiments with PlanetLab and real cloud storage services, demonstrating that we can bind fetched data to the location originally hosting it with high precision. We geolocate data hosted on the majority of our PlanetLab targets to regions no larger than 118,000 km^2, and we geolocate data hosted on Amazon S3 to an area no larger than 12,000 km^2, sufficiently small to identify the state or service region.
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