Smooth Anonymity for Sparse Graphs

TL;DR

This paper introduces smooth-$k$-anonymity, a privacy-preserving method for sparse datasets like networks, overcoming limitations of differential privacy, and demonstrates its effectiveness through theoretical analysis and empirical evaluation.

Contribution

It proposes smooth-$k$-anonymity as a new privacy notion for sparse data and provides scalable algorithms with proven guarantees.

Findings

Theoretical proof that differential privacy offers weak guarantees for sparse datasets.

Efficient algorithms for smooth-$k$-anonymity with strong privacy guarantees.

Improved machine learning performance on anonymized sparse data.

Abstract

When working with user data providing well-defined privacy guarantees is paramount. In this work, we aim to manipulate and share an entire sparse dataset with a third party privately. In fact, differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets, e.g. sparse networks, as one of our main results, we prove that \emph{any} differentially private mechanism that maintains a reasonable similarity with the initial dataset is doomed to have a very weak privacy guarantee. In such situations, we need to look into other privacy notions such as $k$-anonymity. In this work, we consider a variation of $k$-anonymity, which we call smooth-$k$-anonymity, and design simple large-scale algorithms that efficiently provide smooth-$k$-anonymity. We further perform an empirical evaluation to back our theoretical guarantees and show that our algorithm improves the performance in downstream machine learning tasks on anonymized data.