论文标题
广泛低估了差异私人库中灵敏度以及如何修复它的灵敏度
Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It
论文作者
论文摘要
我们在实现差异隐私方面确定了一类新的漏洞。具体而言，由于使用有限的数据类型（即INT或Floats）实现的算术之间的差异，并且在计算基本统计数据（例如总和）时，它们会出现。这些差异会导致实施的统计数据的灵敏度（即一个人的数据会影响结果多少）比我们期望的灵敏度大得多。因此，从本质上讲，所有差异隐私库都无法引入足够的噪音来满足差异隐私的要求，我们表明这可以在现实的攻击中被利用，这些攻击可以从私人查询系统中提取个人级别的信息。除了呈现这些漏洞外，我们还提供了许多解决方案，这些解决方案可以修改或约束实现总和的方式，以恢复灵敏度的理想化或近乎思想化的界限。
We identify a new class of vulnerabilities in implementations of differential privacy. Specifically, they arise when computing basic statistics such as sums, thanks to discrepancies between the implemented arithmetic using finite data types (namely, ints or floats) and idealized arithmetic over the reals or integers. These discrepancies cause the sensitivity of the implemented statistics (i.e., how much one individual's data can affect the result) to be much larger than the sensitivity we expect. Consequently, essentially all differential privacy libraries fail to introduce enough noise to meet the requirements of differential privacy, and we show that this may be exploited in realistic attacks that can extract individual-level information from private query systems. In addition to presenting these vulnerabilities, we also provide a number of solutions, which modify or constrain the way in which the sum is implemented in order to recover the idealized or near-idealized bounds on sensitivity.
