As a staple of data analysis and unsupervised learning, the problem of
private clustering has been widely studied under various privacy models.
Centralized differential privacy is the first of them, and the problem has also
been studied for the local and the shuffle variation. In each case, the goal is
to design an algorithm that computes privately a clustering, with the smallest
possible error. The study of each variation gave rise to new algorithms: the
landscape of private clustering algorithms is therefore quite intricate.
In this paper, we show that a 20-year-old algorithm can be slightly modified
to work for any of these models. This provides a unified picture: while
matching almost all previously known results, it allows us to improve some of
them and extend it to a new privacy model, the continual observation setting,
where the input is changing over time and the algorithm must output a new
solution at each time step.

20 年前的算法经过轻微修改，适用于各种隐私模型，匹配几乎所有已知结果，改进了一些结果并扩展到新的隐私模型，即连续观测环境。

使旧事焕新：差分隐私聚类的统一算法

Making Old Things New: A Unified Algorithm for Differentially Private  Clustering

Deep neural networks (DNNs) are one of the most widely used machine learning
algorithm. DNNs requires the training data to be available beforehand with true
labels. This is not feasible for many real-world problems where data arrives in
the streaming form and acquisition of true labels are scarce and expensive. In
the literature, not much focus has been given to the privacy prospect of the
streaming data, where data may change its distribution frequently. These
concept drifts must be detected privately in order to avoid any disclosure risk
from DNNs. Existing privacy models use concept drift detection schemes such
ADWIN, KSWIN to detect the drifts. In this paper, we focus on the notion of
integrally private DNNs to detect concept drifts. Integrally private DNNs are
the models which recur frequently from different datasets. Based on this, we
introduce an ensemble methodology which we call 'Integrally Private Drift
Detection' (IPDD) method to detect concept drift from private models. Our IPDD
method does not require labels to detect drift but assumes true labels are
available once the drift has been detected. We have experimented with binary
and multi-class synthetic and real-world data. Our experimental results show
that our methodology can privately detect concept drift, has comparable utility
(even better in some cases) with ADWIN and outperforms utility from different
levels of differentially private models. The source code for the paper is
available
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深度神经网络（DNNs）是最广泛使用的机器学习算法之一。本文关注集成私有 DNNs 的概念来检测概念漂移，并引入一种名为 IPDD 的集成方法，不需要标签即可检测漂移。实验证明该方法在隐私性和效用上具有可比性。

使用整体私有模型集的概念漂移检测

Concept Drift Detection using Ensemble of Integrally Private Models

Accurately predicting whether an image is private before sharing it online is
difficult due to the vast variety of content and the subjective nature of
privacy itself. In this paper, we evaluate privacy models that use objects
extracted from an image to determine why the image is predicted as private. To
explain the decision of these models, we use feature-attribution to identify
and quantify which objects (and which of their features) are more relevant to
privacy classification with respect to a reference input (i.e., no objects
localised in an image) predicted as public. We show that the presence of the
person category and its cardinality is the main factor for the privacy
decision. Therefore, these models mostly fail to identify private images
depicting documents with sensitive data, vehicle ownership, and internet
activity, or public images with people (e.g., an outdoor concert or people
walking in a public space next to a famous landmark). As baselines for future
benchmarks, we also devise two strategies that are based on the person presence
and cardinality and achieve comparable classification performance of the
privacy models.

利用从图像中提取的对象来确定图像为私人图像的难度比较大。通过使用特征归属来解释这些模型的决策，我们发现人类类别和其个数是决定隐私决策的主要因素。因此，这些模型在识别包含敏感数据的私人图像、车辆所有权和互联网活动的图像，以及含有人类的公共图像时通常失败。为了将来的基准测试，我们还设计了两个基于人类出现和数量的策略，并实现了与隐私模型相当的分类性能。