# Smooth Anonymity for Sparse Graphs

arXiv: https://arxiv.org/abs/2207.06358 · HTML: https://mlsanity.com/paper/2207.06358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [Hashing for Similarity Search: A Survey](https://mlsanity.com/paper/1408.2927/) (2014)

## Abstract (arXiv)

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 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.