The Data Minimization Principle in Machine Learning (2405.19471v1)

Abstract: 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.

• The paper formalizes data minimization as a bi-level optimization problem that reduces dataset size while preserving model accuracy.
• The paper compares various optimization algorithms and finds that evolutionary algorithms most effectively minimize data without major performance losses.
• The paper demonstrates that minimizing data alone does not ensure lower privacy risks, underscoring the need for enhanced privacy measures.

The Data Minimization Principle in Machine Learning: An Academic Overview

The paper "The Data Minimization Principle in Machine Learning" by Prakhar Ganesh et al. explores the practical implementation of the principle of data minimization, which seeks to lessen the amount of collected, processed, and retained data to mitigate privacy risks. This research introduces a formal optimization framework for data minimization grounded in legal definitions and evaluates its efficacy and privacy implications.

Contributions and Key Questions

The paper addresses several critical questions related to the principle of data minimization in ML:

1. Faithfulness to Data Protection Regulations: By examining global data protection regulations, the paper introduces a formalization of data minimization as an optimization problem incorporating the individualized nature of minimization.
2. Impact of Different Algorithms on Minimized Data: Various classes of optimization algorithms are adapted to solve the data minimization problem, with an extensive evaluation focusing on emergent individualization and multiplicity.
3. Alignment with Privacy Expectations: The researchers introduce multiple threat models to quantify privacy risks of the minimized datasets, revealing that data minimization alone may not meet privacy expectations.
4. Augmentation of Data Minimization Algorithms for Privacy: Effective modifications to the data minimization algorithms are proposed to enhance their privacy-preserving abilities, demonstrating improved trade-offs between user privacy and utility.

Formal Framework for Data Minimization

The authors define the data minimization problem in ML as a bi-level optimization problem aiming to balance dataset size reduction and model quality retention. The formalization is captured by minimizing the dataset size while ensuring that model performance, as measured on the original dataset, does not degrade beyond a specified threshold $\alpha$.

Experimental Evaluation and Findings

Data Minimization Algorithms

Three baseline methods (feature selection, random subsampling, and individualized random subsampling) and three optimization algorithms based on bi-level optimization literature (approximating the target utility, modeling the target utility, and evolutionary algorithms) are compared. The experimental results reveal that a substantial amount of data can often be removed without a significant drop in utility. Among the optimization algorithms, evolutionary algorithms demonstrate the highest efficacy in minimizing data across datasets.

Privacy Implications

Privacy risks, such as re-identification risk and reconstruction risk, are assessed using extensive evaluations. The empirical results show that:

• Re-identification Risk: Despite reducing dataset size significantly, re-identification risks do not necessarily decrease proportionally, indicating a misalignment between data minimization and expected privacy outcomes. Feature selection, in particular, shows poor alignment with re-identification risks across all datasets.
• Reconstruction Risk: The evaluation shows that high reconstruction risks persist even at high levels of data minimization, attributed to the inherent correlations among features in real-world datasets.

Aligning Data Minimization with Privacy

The researchers propose a modified approach to data minimization by incorporating feature-level privacy scores. These scores help in further adapting the minimization algorithm to reduce the associated privacy risks.

• Re-identification: Using privacy scores derived from the uniqueness of features, the modified algorithm better aligns with re-identification risk reduction.
• Reconstruction: Privacy scores based on feature correlations show improvements in mitigating reconstruction risks.

Implications and Future Directions

The paper underscores the necessity for models that operationalize the principle of data minimization while incorporating privacy considerations. The findings highlight the critical need for minimization strategies that account for privacy risks rather than assuming an implicit privacy enhancement. Future research directions include:

• Development of Efficient Minimization Algorithms: To address large-scale applications, future work should aim to develop algorithms that balance utility and privacy effectively.
• Fairness-Aware Minimization: Ensuring that minimization practices do not disproportionately affect minority groups, thus developing fairness-aware approaches.
• Ethical and Legal Considerations: Exploring the broader impact of data minimization practices in terms of legal compliance and ethical considerations.

In conclusion, this research marks an important step in bridging the gap between legal data minimization mandates and their practical implementation in ML systems, advocating for a more nuanced approach that truly respects individual privacy.