# Triangle Counting with Local Edge Differential Privacy

arXiv: https://arxiv.org/abs/2305.02263 · HTML: https://mlsanity.com/paper/2305.02263/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2023.

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

- [Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts](https://mlsanity.com/paper/2507.14835/) (2025)
- [Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions](https://mlsanity.com/paper/2312.07055/) (2023)
- [Signal Recovery from Random Dot-Product Graphs Under Local Differential Privacy](https://mlsanity.com/paper/2504.17274/) (2025)
- [Differentially Private Range Subgraph Counting](https://mlsanity.com/paper/2606.08179/) (2026)

## Abstract (arXiv)

Many deployments of differential privacy in industry are in the local model, where each party releases its private information via a differentially private randomizer. We study triangle counting in the local model with edge differential privacy (that, intuitively, requires that the outputs of the algorithm on graphs that differ in one edge be indistinguishable). In this model, each party's local view consists of the adjacency list of one vertex. We investigate both noninteractive and interactive variants of the model. In the noninteractive model, we prove that additive Omega(n2) error is necessary for sufficiently small constant varepsilon, where n is the number of nodes and varepsilon is the privacy parameter. This lower bound is our main technical contribution. It uses a reconstruction attack with a new class of linear queries and a novel mix-and-match strategy of running the local randomizers with different completions of their adjacency lists. It matches the additive error of the algorithm based on Randomized Response, proposed by Imola, Murakami and Chaudhuri (USENIX2021) and analyzed by Imola, Murakami and Chaudhuri (CCS2022) for constant varepsilon. We use a different postprocessing of Randomized Response and provide tight bounds on the variance of the resulting algorithm. In the interactive setting, we prove a lower bound of Omega(n3/2/varepsilon) on the additive error for varepsilon≤ 1. Previously, no hardness results were known for interactive, edge-private algorithms in the local model, except for those that follow trivially from the results for the central model. Our work significantly improves on the state of the art in differentially private graph analysis in the local model.