OPAL: High performance platform for large-scale privacy-preserving location data analytics
OPAL: High performance platform for large-scale privacy-preserving location data analytics
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DOI：
10.1109/bigdata47090.2019.9006389
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
A. Oehmichen;Shubham Jain;Andrea Gadotti;Yves-Alexandre de Montjoye
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文献类型：
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作者：
A. Oehmichen;Shubham Jain;Andrea Gadotti;Yves-Alexandre de Montjoye
移动的电话和其他无处不在的技术正在产生大量的高分辨率位置数据。这一数据已被证明对公共利益具有巨大潜力，例如监测危机期间的人口移徙或预测流行病的传播。然而，位置数据被认为是最敏感的数据类型之一，大量研究表明，传统的大数据匿名方法存在局限性。到目前为止，隐私问题强烈限制了电信公司收集的位置数据的使用，特别是在发展中国家。在本文中，我们介绍了OPAL（用于OPEn AL出租），一个开源的，可扩展的，隐私保护的位置数据平台。在其核心，OPAL依赖于一个开放的算法，从位置数据中提取关键的聚合统计数据，用于广泛的潜在用例。我们首先讨论我们如何设计OPAL平台，构建一个模块化和弹性的框架，以实现高效的位置分析。然后，我们描述了分层的机制，我们已经到位，以保护隐私，并讨论人口密度算法的例子。最后，我们将评估平台的可扩展性和可扩展性，并讨论相关工作。代码将在发布后在GitHub上开源。
Mobile phones and other ubiquitous technologies are generating vast amounts of high-resolution location data. This data has been shown to have a great potential for the public good, e.g. to monitor human migration during crises or to predict the spread of epidemic diseases. Location data is, however, considered one of the most sensitive types of data, and a large body of research has shown the limits of traditional data anonymization methods for big data. Privacy concerns have so far strongly limited the use of location data collected by telcos, especially in developing countries.In this paper, we introduce OPAL (for OPen ALgorithms), an open-source, scalable, and privacy-preserving platform for location data. At its core, OPAL relies on an open algorithm to extract key aggregated statistics from location data for a wide range of potential use cases. We first discuss how we designed the OPAL platform, building a modular and resilient framework for efficient location analytics. We then describe the layered mechanisms we have put in place to protect privacy and discuss the example of a population density algorithm. We finally evaluate the scalability and extensibility of the platform and discuss related work.The code will be open-sourced on GitHub upon publication.