优化分层数据发布中的隐私预算分配，提升数据可用性。
Optimal Allocation of Privacy Budget on Hierarchical Data Release
- 将隐私预算分配建模为约束优化问题，平衡层级间隐私与效用。
- 实验表明，最优分配可显著提升下游任务性能。
- 适合关注隐私保护与数据效用权衡的研究者。
在保留个体隐私的前提下释放具有分层结构的数据集中的有用信息，是一项重大挑战。标准的隐私保护机制，尤其是差分隐私，通常需要在层次结构的不同层级和组件之间仔细分配有限的隐私预算。次优的分配可能导致噪声过多，使数据无用，或对敏感信息保护不足。本文解决了分层数据发布中隐私预算最优分配的关键问题。将该挑战形式化为一个约束优化问题，旨在在总隐私预算限制下最大化数据效用，同时考虑数据粒度与隐私损失之间的内在权衡。所提方法得到理论分析支持，并通过在真实分层数据集上的综合实验验证。实验表明，最优隐私预算分配能显著提高释放数据的效用，并改善下游任务的表现。
原文摘要 · Abstract (English)
Releasing useful information from datasets with hierarchical structures while preserving individual privacy presents a significant challenge. Standard privacy-preserving mechanisms, and in particular Differential Privacy, often require careful allocation of a finite privacy budget across different levels and components of the hierarchy. Sub-optimal allocation can lead to either excessive noise, rendering the data useless, or to insufficient protections for sensitive information. This paper addresses the critical problem of optimal privacy budget allocation for hierarchical data release. It formulates this challenge as a constrained optimization problem, aiming to maximize data utility subject to a total privacy budget while considering the inherent trade-offs between data granularity and privacy loss. The proposed approach is supported by theoretical analysis and validated through comprehensive experiments on real hierarchical datasets. These experiments demonstrate that optimal privacy budget allocation significantly enhances the utility of the released data and improves the performance of downstream tasks.
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