论文标题
概率计数器以保存数据汇总的隐私
Probabilistic Counters for Privacy Preserving Data Aggregation
论文作者
论文摘要
概率计数器是经常用于空间效率设置的基数估算的知名工具。在本文中，我们从保留隐私的角度研究了概率反驳。我们使用标准的，僵化的差异隐私概念。直觉是，概率计数器没有透露太多有关个人的信息，而仅提供有关人口的一般信息。因此，可以安全地使用它们，而不会违反个人的隐私。但是，事实证明，对概率计数器的隐私参数进行精确的正式分析非常困难，需要高级技术和非常仔细的方法。 我们证明，概率计数器可以用作无随机性的隐私保护机制。即，即使概率计数器多次使用，该协议的固有随机化也足以保护隐私。特别是，我们提出了基于Morris Counter和Maxgeo Counter的特定保护数据聚合协议。一些提出的结果专门用于从隐私保护的角度来看，这些柜台尚未对其进行调查。另一部分是改善以前的结果。我们展示了我们的结果如何用于执行分布式调查并比较基于计数器解决方案的属性和标准的拉普拉斯方法。
Probabilistic counters are well-known tools often used for space-efficient set cardinality estimation. In this paper, we investigate probabilistic counters from the perspective of preserving privacy. We use the standard, rigid differential privacy notion. The intuition is that the probabilistic counters do not reveal too much information about individuals but provide only general information about the population. Therefore, they can be used safely without violating the privacy of individuals. However, it turned out, that providing a precise, formal analysis of the privacy parameters of probabilistic counters is surprisingly difficult and needs advanced techniques and a very careful approach. We demonstrate that probabilistic counters can be used as a privacy protection mechanism without extra randomization. Namely, the inherent randomization from the protocol is sufficient for protecting privacy, even if the probabilistic counter is used multiple times. In particular, we present a specific privacy-preserving data aggregation protocol based on Morris Counter and MaxGeo Counter. Some of the presented results are devoted to counters that have not been investigated so far from the perspective of privacy protection. Another part is an improvement of previous results. We show how our results can be used to perform distributed surveys and compare the properties of counter-based solutions and a standard Laplace method.
