Large-scale collection of contextual information is often essential in order to gather statistics, train machine learning models, and extract knowledge from data. The ability to do so in a {\em privacy-preserving} way -- i.e., without collecting fine-grained user data -- enables a number of additional computational scenarios that would be hard, or outright impossible, to realize without strong privacy guarantees. In this paper, we present the design and implementation of practical techniques for privately gathering statistics from large data streams. We build on efficient cryptographic protocols for private aggregation and on data structures for succinct data representation, namely, Count-Min Sketch and Count Sketch. These allow us to reduce the communication and computation complexity incurred by each data source (e.g., end-users) from linear to logarithmic in the size of their input, while introducing a parametrized upper-bounded error that does not compromise the quality of the statistics. We then show how to use our techniques, efficiently, to instantiate real-world privacy-friendly systems, supporting recommendations for media streaming services, prediction of user locations, and computation of median statistics for Tor hidden services.
A Locally Differential Private Coding-Assisted Succinct Histogram Protocol
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA（电气工程与计算机科学系，密歇根大学，安阿伯，MI 48109, USA）；Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA（电气与计算机工程系，东北大学，波士顿，MA 02115, USA）
Local, Private, Efficient Protocols for Succinct Histograms
The Pennsylvania State University（宾夕法尼亚州立大学）；Boston University（波士顿大学）；Harvard University（哈佛大学）
我们给出了有效的协议和匹配精度下界的频率估计的局部模型的差分隐私。在这个模型中，个人用户自己随机化他们的数据，将不同的私人报告发送到一个不受信任的服务器，该服务器将这些报告聚集在一起。我们研究的协议，产生一个简洁的直方图表示的数据。一个简洁的直方图是数据中最频繁的项目（通常称为“重打击者”）及其频率估计值的列表;所有其他项目的频率隐式估计为0。如果有$n$用户的项目来自一个大小为$d$的宇宙，我们的协议在$n$和$\log(d)$中以时间多项式运行。以高概率，他们估计每个项目的准确性，直到错误$O\left(\sqrt{\log(d)/(ε^2n)}\right)$，其中$ε$是隐私参数。此外，我们表明，这么多的错误是必要的，无论计算效率，甚至对于简单的设置，只有一个项目出现在数据集中的显着频率。先前用于该任务的方案（Mishra和Sandler，2006; Hsu，Khanna和Roth，2012）要么在$Ω(d)$中运行，要么具有更严重的错误（约为$\sqrt[6]{\log(d)/(ε^2n)}$），并且唯一已知的错误下限是$Ω(1/\sqrt{n})$。我们还将McGregor et al（2010）的结果适用于当地环境。在一个使用公共硬币的模型中，我们展示了每个用户只需要向服务器发送1比特。对于所有已知的本地协议（包括我们的），转换保持计算效率。
Department of Electronic Engineering（电子工程系）；Fudan University（复旦大学）；School of Computing（计算学院）；Macquarie University（麦觉瑞大学）；Department of Communication Science and Engineering（通信科学与工程系）
Privacy-preserving Blockchain-enabled Parametric Insurance via Remote Sensing and IoT
School of Computing, Australian National University（澳大利亚国立大学计算学院）；Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）；Data61, CSIRO（CSIRO数据61）
