Shortly after it was first introduced in 2006, differential privacy became
the flagship data privacy definition. Since then, numerous variants and
extensions were proposed to adapt it to different scenarios and attacker
models. In this work, we propose a systematic taxonomy of these variants and
extensions. We list all data privacy definitions based on differential privacy,
and partition them into seven categories, depending on which aspect of the
original definition is modified.
These categories act like dimensions: variants from the same category cannot
be combined, but variants from different categories can be combined to form new
definitions. We also establish a partial ordering of relative strength between
these notions by summarizing existing results. Furthermore, we list which of
these definitions satisfy some desirable properties, like composition,
post-processing, and convexity by either providing a novel proof or collecting
existing ones.

本文提出一种基于差分隐私的数据隐私定义的分类法，将已有的定义分为七类，依据对原始定义的哪些方面进行了修改。这些类别类似于维度：来自同一类别的变体不能组合，但来自不同类别的变体可以组合形成新的定义，并列举哪些定义满足一些期望的性质，如合成、后处理和凸性，为此提供一些新的证明或收集现有证明。

SoK：差分隐私

SoK: Differential Privacies

Privacy definitions provide ways for trading-off the privacy of individuals
in a statistical database for the utility of downstream analysis of the data.
In this paper, we present Blowfish, a class of privacy definitions inspired by
the Pufferfish framework, that provides a rich interface for this trade-off. In
particular, we allow data publishers to extend differential privacy using a
policy, which specifies (a) secrets, or information that must be kept secret,
and (b) constraints that may be known about the data. While the secret
specification allows increased utility by lessening protection for certain
individual properties, the constraint specification provides added protection
against an adversary who knows correlations in the data (arising from
constraints). We formalize policies and present novel algorithms that can
handle general specifications of sensitive information and certain count
constraints. We show that there are reasonable policies under which our privacy
mechanisms for k-means clustering, histograms and range queries introduce
significantly lesser noise than their differentially private counterparts. We
quantify the privacy-utility trade-offs for various policies analytically and
empirically on real datasets.

介绍了 Blowfish 隐私定义的概念及其在差分隐私中的应用，其中包括可以扩展差分隐私的策略、数据发布者可以指定必须保密的信息以及相关的数据约束条件，并使用策略及新颖算法在 k-means 聚类、直方图和范围查询领域中实现隐私和效用的平衡。

Blowfish Privacy: 使用策略调整隐私和效用的权衡

Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies

Over the last decade there have been great strides made in developing
techniques to compute functions privately. In particular, Differential Privacy
gives strong promises about conclusions that can be drawn about an individual.
In contrast, various syntactic methods for providing privacy (criteria such as
kanonymity and l-diversity) have been criticized for still allowing private
information of an individual to be inferred. In this report, we consider the
ability of an attacker to use data meeting privacy definitions to build an
accurate classifier. We demonstrate that even under Differential Privacy, such
classifiers can be used to accurately infer "private" attributes in realistic
data. We compare this to similar approaches for inferencebased attacks on other
forms of anonymized data. We place these attacks on the same scale, and observe
that the accuracy of inference of private attributes for Differentially Private
data and l-diverse data can be quite similar.

通过比较差分隐私和 l - 多样性数据的准确分类器推断私人属性，研究发现即使采用差分隐私，也能够推断出个体的隐私属性。