# PriME: Privacy-aware Membership profile Estimation in networks

arXiv: https://arxiv.org/abs/2406.02794 · HTML: https://mlsanity.com/paper/2406.02794/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [Node-private community estimation in stochastic block models: Tractable algorithms and lower bounds](https://mlsanity.com/paper/2605.15943/) (2026)
- [Signal Recovery from Random Dot-Product Graphs Under Local Differential Privacy](https://mlsanity.com/paper/2504.17274/) (2025)
- [Privacy utility trade offs for parameter estimation in degree heterogeneous higher order networks](https://mlsanity.com/paper/2602.03948/) (2026)
- [Near-optimal node-private community estimation in polynomial-time](https://mlsanity.com/paper/2607.09441/) (2026)

## Abstract (arXiv)

This paper presents a novel approach to estimating community membership probabilities for network vertices generated by the Degree Corrected Mixed Membership Stochastic Block Model while preserving individual edge privacy. Operating within the varepsilon-edge local differential privacy framework, we introduce an optimal private algorithm based on a symmetric edge flip mechanism and spectral clustering for accurate estimation of vertex community memberships. We conduct a comprehensive analysis of the estimation risk and establish the optimality of our procedure by providing matching lower bounds to the minimax risk under privacy constraints. To validate our approach, we demonstrate its performance through numerical simulations and its practical application to real-world data. This work represents a significant step forward in balancing accurate community membership estimation with stringent privacy preservation in network data analysis.