The principle of data minimization aims to reduce the amount of data
collected, processed or retained to minimize the potential for misuse,
unauthorized access, or data breaches. Rooted in privacy-by-design principles,
data minimization has been endorsed by various global data protection
regulations. However, its practical implementation remains a challenge due to
the lack of a rigorous formulation. This paper addresses this gap and
introduces an optimization framework for data minimization based on its legal
definitions. It then adapts several optimization algorithms to perform data
minimization and conducts a comprehensive evaluation in terms of their
compliance with minimization objectives as well as their impact on user
privacy. Our analysis underscores the mismatch between the privacy expectations
of data minimization and the actual privacy benefits, emphasizing the need for
approaches that account for multiple facets of real-world privacy risks.

基于法律定义，本文介绍了数据最小化的优化框架，并使用多个优化算法来实现数据最小化，通过对合规性和用户隐私影响的全面评估，我们的分析强调了数据最小化的隐私预期与实际隐私利益之间的不匹配，强调了需要考虑现实世界隐私风险的多个方面的方法的需求。

机器学习中的数据最小化原则

The Data Minimization Principle in Machine Learning

Aiming to train and deploy predictive models, organizations collect large
amounts of detailed client data, risking the exposure of private information in
the event of a breach. To mitigate this, policymakers increasingly demand
compliance with the data minimization (DM) principle, restricting data
collection to only that data which is relevant and necessary for the task.
Despite regulatory pressure, the problem of deploying machine learning models
that obey DM has so far received little attention. In this work, we address
this challenge in a comprehensive manner. We propose a novel vertical DM (vDM)
workflow based on data generalization, which by design ensures that no
full-resolution client data is collected during training and deployment of
models, benefiting client privacy by reducing the attack surface in case of a
breach. We formalize and study the corresponding problem of finding
generalizations that both maximize data utility and minimize empirical privacy
risk, which we quantify by introducing a diverse set of policy-aligned
adversarial scenarios. Finally, we propose a range of baseline vDM algorithms,
as well as Privacy-aware Tree (PAT), an especially effective vDM algorithm that
outperforms all baselines across several settings. We plan to release our code
as a publicly available library, helping advance the standardization of DM for
machine learning. Overall, we believe our work can help lay the foundation for
further exploration and adoption of DM principles in real-world applications.

通过数据泛化提出了基于垂直数据最小化的工作流，旨在训练和部署遵循数据最小化原则的机器学习模型，以减少数据泄露风险并保护客户隐私。提出了一种基于政策对齐的对抗场景量化方法，并设计了一种特别有效的垂直数据最小化算法。

从原则到实践：垂直数据最小化用于机器学习

From Principle to Practice: Vertical Data Minimization for Machine  Learning

Article 5(1)(c) of the European Union's General Data Protection Regulation
(GDPR) requires that "personal data shall be [...] adequate, relevant, and
limited to what is necessary in relation to the purposes for which they are
processed (`data minimisation')". To date, the legal and computational
definitions of `purpose limitation' and `data minimization' remain largely
unclear. In particular, the interpretation of these principles is an open issue
for information access systems that optimize for user experience through
personalization and do not strictly require personal data collection for the
delivery of basic service.
In this paper, we identify a lack of a homogeneous interpretation of the data
minimization principle and explore two operational definitions applicable in
the context of personalization. The focus of our empirical study in the domain
of recommender systems is on providing foundational insights about the (i)
feasibility of different data minimization definitions, (ii) robustness of
different recommendation algorithms to minimization, and (iii) performance of
different minimization strategies.We find that the performance decrease
incurred by data minimization might not be substantial, but that it might
disparately impact different users---a finding which has implications for the
viability of different formal minimization definitions. Overall, our analysis
uncovers the complexities of the data minimization problem in the context of
personalization and maps the remaining computational and regulatory challenges.

本文探讨在个性化信息获取系统中 GDPR 中 `data minimization' 的执行，通过实证研究找到了不同执行方式的可行性，对不同用户产生的影响和表现结果进行分析，揭示了在此背景下数据最小化问题的复杂性和尚存的计算和监管挑战。