Scalable contribution bounding to achieve privacy

TL;DR

This paper introduces a scalable, distributed algorithm for contribution bounding in user-level differential privacy, modeling ownership as a hypergraph to efficiently limit user contributions while maximizing dataset utility.

Contribution

It presents a novel hypergraph-based distributed algorithm that efficiently enforces contribution bounds, overcoming scalability issues of previous methods.

Findings

Algorithm scales to large datasets

Maximizes dataset utility under privacy constraints

Ensures user contribution limits are respected

Abstract

In modern datasets, where single records can have multiple owners, enforcing user-level differential privacy requires capping each user's total contribution. This "contribution bounding" becomes a significant combinatorial challenge. Existing sequential algorithms for this task are computationally intensive and do not scale to the massive datasets prevalent today. To address this scalability bottleneck, we propose a novel and efficient distributed algorithm. Our approach models the complex ownership structure as a hypergraph, where users are vertices and records are hyperedges. The algorithm proceeds in rounds, allowing users to propose records in parallel. A record is added to the final dataset only if all its owners unanimously agree, thereby ensuring that no user's predefined contribution limit is violated. This method aims to maximize the size of the resulting dataset for high utility while providing a practical, scalable solution for implementing user-level privacy in large, real-world systems.