通过缩放定律学习限制数据汇集--数据最小化法律原则的计算解释
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
摘要：
现代机器学习系统越来越多地以广泛的个人数据汇集为特征，尽管这种做法的回报越来越少，社会成本越来越高。然而，数据最小化是欧盟《通用数据保护条例》（“GDPR”）中规定的核心数据保护原则之一，要求仅处理足够、相关且限于必要范围的个人数据。然而，由于缺乏技术解释，这一原则的采用有限。在这项工作中，我们建立在机器学习和法律的文献，提出FIDO，一个框架，用于抑制数据过度收集。FIDO学习基于与系统性能相关的数据最小化的解释来限制数据汇集。具体地，FIDO通过在获取数据时迭代地更新性能曲线的估计或数据集大小与性能之间的关系来提供数据汇集停止标准。FIDO通过分段幂律技术来估计性能曲线，该技术在整个数据汇集中分别对算法性能的不同阶段进行建模。实证实验表明，该框架产生准确的性能曲线和跨数据集和特征获取算法的数据汇集停止标准。我们进一步证明，许多其他系列的曲线系统地高估了额外数据的回报。从我们的调查结果和分析提供了更深入的了解设计数据最小化框架时的相关考虑因素，包括主动特征采集对个人用户的影响和用户特定的数据最小化的可行性。最后，我们提出了数据最小化的实际建议。
英文摘要：
Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practices. Yet, data minimisation is one of the core data protection principles enshrined in the European Union's General Data Protection Regulation ('GDPR') and requires that only personal data that is adequate, relevant and limited to what is necessary is processed. However, the principle has seen limited adoption due to the lack of technical interpretation. In this work, we build on literature in machine learning and law to propose FIDO, a Framework for Inhibiting Data Overcollection. FIDO learns to limit data collection based on an interpretation of data minimization tied to system performance. Concretely, FIDO provides a data collection stopping criterion by iteratively updating an estimate of the performance curve, or the relationship between dataset size and performance, as data is acquired. FIDO estimates the performance curve via a piecewise power law technique that models distinct phases of an algorithm's performance throughout data collection separately. Empirical experiments show that the framework produces accurate performance curves and data collection stopping criteria across datasets and feature acquisition algorithms. We further demonstrate that many other families of curves systematically overestimate the return on additional data. Results and analysis from our investigation offer deeper insights into the relevant considerations when designing a data minimization framework, including the impacts of active feature acquisition on individual users and the feasability of user-specific data minimization. We conclude with practical recommendations for the implementation of data minimization.
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