Optimal Allocation of Privacy Budget on Hierarchical Data Release

TL;DR

This paper proposes a method to optimally allocate privacy budgets in hierarchical data releases, balancing data utility and privacy, supported by theoretical analysis and experiments on real datasets.

Contribution

It formulates the privacy budget allocation as a constrained optimization problem and provides a solution that improves data utility in hierarchical privacy-preserving releases.

Findings

Optimal budget allocation enhances data utility.

The approach outperforms uniform allocation methods.

Improved performance in downstream tasks.

Abstract

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.