论文标题
机会主义的多方改组，用于数据报告隐私
Opportunistic multi-party shuffling for data reporting privacy
论文作者
论文摘要
涉及自愿参与者的数据收集框架的一个重要特征是隐私。除了数据加密外，在通信通道受到损害的情况下保护数据免受第三方的保护外，还有一些方案来混淆数据，从而在数据本身中提供一些匿名性，以及“混合”数据以防止通过使用网络标识符将数据追溯回源的方案。这种混合通常是通过在数据收集框架中利用特殊混合网络来实现的。在本文中，我们专注于混合参与者不需要信任混合网络或隐藏数据源的数据收集器的数据，以便我们可以评估同伴对现实世界中同行混合策略的效力。为了实现这一目标，我们提出了一个简单的机会性多方改组方案，以混合数据并有效地混淆数据源。我们成功地使用人工参数模拟了3个案例，然后使用现实世界中的移动数据挑战（MDC）数据来模拟具有现实参数的另外2个方案。我们的结果表明，此类方法可以根据数据收集的时间限制有效，并且我们以设计含义为实施现实生活部署中所提出的数据收集方案的设计含义。
An important feature of data collection frameworks, in which voluntary participants are involved, is that of privacy. Besides data encryption, which protects the data from third parties in case the communication channel is compromised, there are schemes to obfuscate the data and thus provide some anonymity in the data itself, as well as schemes that 'mix' the data to prevent tracing the data back to the source by using network identifiers. This mixing is usually implemented by utilizing special mix networks in the data collection framework. In this paper we focus on schemes for mixing the data where the participants do not need to trust the mix network or the data collector with hiding the source of the data so that we can evaluate the efficacy of peer to peer mixing strategies in the real world. To achieve this, we present a simple opportunistic multi-party shuffling scheme to mix the data and effectively obfuscate the source of the data. We successfully simulate 3 cases with artificial parameters and then use the real-world Mobile Data Challenge (MDC) data to simulate an additional 2 scenarios with realistic parameters. Our results show that such approaches can be effective depending on the time constraints of the data collection and we conclude with design implications for the implementation of the proposed data collection scheme in real life deployments.
