安全的历史记录：在保护个人隐私的同时计算加密数据的群体统计数据
Secured histories: computing group statistics on encrypted data while preserving individual privacy
摘要：
随着传感器变得越来越普遍，越来越多的关于我们每个人的信息将被收集。一个长期的研究问题是如何最好地支持有益的使用，同时保护个人隐私。在线状态系统是支持协作的新兴应用类别。这些系统利用无处不在的传感器来估计最终用户的位置、活动和可用的通信渠道。由于这些在线数据是敏感的，为了实现广泛的采用，共享模型必须反映用户的隐私和共享偏好。为了反映用户的协作关系和共享需求，我们引入了CollaPSE安全性，其中个人可以完全访问自己的数据，第三方处理数据而无需了解任何有关数据值的信息，并且层次结构中较高的用户仅了解有关其下属员工的统计信息。我们描述了简单的计划，有效地实现CollaPSE安全的时间序列数据。我们使用现成的加密函数实现了这些协议，并将这些协议与FXPAL的myUnity呈现系统集成。
英文摘要：
As sensors become ever more prevalent, more and more information will be collected about each of us. A longterm research question is how best to support beneficial uses while preserving individual privacy. Presence systems are an emerging class of applications that support collaboration. These systems leverage pervasive sensors to estimate end-user location, activities, and available communication channels. Because such presence data are sensitive, to achieve wide-spread adoption, sharing models must reflect the privacy and sharing preferences of the users. To reflect users' collaborative relationships and sharing desires, we introduce CollaPSE security, in which an individual has full access to her own data, a third party processes the data without learning anything about the data values, and users higher up in the hierarchy learn only statistical information about the employees under them. We describe simple schemes that efficiently realize CollaPSE security for time series data. We implemented these protocols using readily available cryptographic functions, and integrated the protocols with FXPAL's myUnity presence system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
