Privacy-preserving community sensing for medical research with duplicated perturbation
Privacy-preserving community sensing for medical research with duplicated perturbation
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DOI：
10.1109/icc.2014.6883988
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
Shunsuke Aoki;K. Sezaki
中科院分区：
文献类型：
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作者：
Shunsuke Aoki;K. Sezaki
社区感知是一种新兴的范式，它使越来越多的移动设备用户能够共享自己收集的详细统计数据。特别是，该系统预计将用于医学和公共卫生研究，因为这些移动设备几乎在任何时候都离用户很近。然而，由于移动设备收集用户的敏感信息，一些隐私问题将阻碍用于医学研究的社区传感应用的传播。因此，我们需要一个能够让普通用户加入社区移动感知的环境。在移动侦听中用于保护隐私的一种广为人知的技术是数据扰动，其在用户侧增加噪声并允许中央服务器重建原始数据的统计。在本文中，我们回顾了最新的扰动方案的一个关键漏洞，在该漏洞中，恶意攻击者可以通过长期监控攻击从被扰动的数据中恢复用户的敏感信息。为了克服这种脆弱性，我们提出了多维随机响应的隐私保护社区感知，其中所有感知的数据都被处理了两次。使用我们的方案，我们能够安全地收集用户的医疗信息。我们评估了我们的方案如何在保护隐私的同时保持聚合信息的数据完整性。
Community sensing is an emerging paradigm that enables the increasing number of mobile device users to share the minute statistics collected by themselves. In particular, this system is expected to be used for medical and public health research studies, as these mobile devices are in close proximity of the users almost at all times. However, since the mobile devices collect users' sensitive information, a number of privacy concerns will hinder the spread of community sensing applications for medical research. Therefore, we require an environment that enables general users to join community mobile sensing. A widely known technique for preserving privacy in mobile sensing is data perturbation, which adds noises on the user side and allows the central server to reconstruct the statistics of the original data. In this paper, we review a critical vulnerability of state-of-the-art perturbation schemes in which a malicious attacker may restore users' sensitive information from the perturbed data through long-term monitoring attacks. To overcome such vulnerability, we propose privacy-preserving community sensing with multidimensional randomized response, in which all sensed data are processed twice. Using our scheme, we are able to collect users' medical information with security. We evaluate how our scheme can preserve privacy while maintaining the data integrity of aggregated information.