# Information-Theoretic Privacy in Distributed Average Consensus

arXiv: https://arxiv.org/abs/1809.01794 · HTML: https://mlsanity.com/paper/1809.01794/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Differentially Private Decentralized Learning with Random Walks](https://mlsanity.com/paper/2402.07471/) (2024)
- [Unified Privacy Guarantees for Decentralized Learning via Matrix Factorization](https://mlsanity.com/paper/2510.17480/) (2025)

## Abstract (arXiv)

We present a distributed average consensus protocol that preserves the privacy of agents' inputs. Unlike the differential privacy mechanisms, the presented protocol does not affect the accuracy of the output. It is shown that the protocol preserves the information-theoretic privacy of the agents' inputs against colluding passive adversarial (or honest-but-curious) agents in the network, if the adversarial agents do not constitute a vertex cut in the underlying communication network. This implies that we can guarantee information-theoretic privacy of all the honest agents' inputs against t arbitrary colluding passive adversarial agents if the network is (t+1)-connected. The protocol is constructed by composing a distributed privacy mechanism that we propose with any (non-private) distributed average consensus algorithm.