This paper presents a nearly tight audit of the Differentially Private
Stochastic Gradient Descent (DP-SGD) algorithm in the black-box model. Our
auditing procedure empirically estimates the privacy leakage from DP-SGD using
membership inference attacks; unlike prior work, the estimates are appreciably
close to the theoretical DP bounds. The main intuition is to craft worst-case
initial model parameters, as DP-SGD's privacy analysis is agnostic to the
choice of the initial model parameters. For models trained with theoretical
$\varepsilon=10.0$ on MNIST and CIFAR-10, our auditing procedure yields
empirical estimates of $7.21$ and $6.95$, respectively, on 1,000-record samples
and $6.48$ and $4.96$ on the full datasets. By contrast, previous work achieved
tight audits only in stronger (i.e., less realistic) white-box models that
allow the adversary to access the model's inner parameters and insert arbitrary
gradients. Our auditing procedure can be used to detect bugs and DP violations
more easily and offers valuable insight into how the privacy analysis of DP-SGD
can be further improved.

该研究通过黑盒模型对 Differentially Private Stochastic Gradient Descent (DP-SGD) 算法进行了近乎严格的审计，通过成员推理攻击经验性地估计了 DP-SGD 的隐私泄漏，并且估计结果接近理论 DP 边界。

近似最紧密的黑盒审计差分隐私机器学习

Nearly Tight Black-Box Auditing of Differentially Private Machine  Learning

People form judgments and make decisions based on the information that they
observe. A growing portion of that information is not only provided, but
carefully curated by social media platforms. Although lawmakers largely agree
that platforms should not operate without any oversight, there is little
consensus on how to regulate social media. There is consensus, however, that
creating a strict, global standard of "acceptable" content is untenable (e.g.,
in the US, it is incompatible with Section 230 of the Communications Decency
Act and the First Amendment).
In this work, we propose that algorithmic filtering should be regulated with
respect to a flexible, user-driven baseline. We provide a concrete framework
for regulating and auditing a social media platform according to such a
baseline. In particular, we introduce the notion of a baseline feed: the
content that a user would see without filtering (e.g., on Twitter, this could
be the chronological timeline). We require that the feeds a platform filters
contain "similar" informational content as their respective baseline feeds, and
we design a principled way to measure similarity. This approach is motivated by
related suggestions that regulations should increase user agency. We present an
auditing procedure that checks whether a platform honors this requirement.
Notably, the audit needs only black-box access to a platform's filtering
algorithm, and it does not access or infer private user information. We provide
theoretical guarantees on the strength of the audit. We further show that
requiring closeness between filtered and baseline feeds does not impose a large
performance cost, nor does it create echo chambers.

该研究探讨如何通过基于用户选择的算法过滤器的灵活管控，以及基这一选定标准进行社交媒体平台的审查和审核，从而平衡监管和自由表达之间的关系。

一个用户驱动的社交媒体监管和审计框架

A User-Driven Framework for Regulating and Auditing Social Media

We introduce KPI-Check, a novel system that automatically identifies and
cross-checks semantically equivalent key performance indicators (KPIs), e.g.
"revenue" or "total costs", in real-world German financial reports. It combines
a financial named entity and relation extraction module with a BERT-based
filtering and text pair classification component to extract KPIs from
unstructured sentences before linking them to synonymous occurrences in the
balance sheet and profit & loss statement. The tool achieves a high matching
performance of $73.00$% micro F$_1$ on a hold out test set and is currently
being deployed for a globally operating major auditing firm to assist the
auditing procedure of financial statements.

KPI-Check 使用 BERT 过滤和文本分类技术，自动识别纠正金融报告中的同义词关键绩效指标，实现了高匹配性能。目前，这个工具正在为一家全球性审计公司的资产负债表和损益表审核工作提供帮助。