# Preserving Statistical Privacy in Distributed Optimization

arXiv: https://arxiv.org/abs/2004.01312 · HTML: https://mlsanity.com/paper/2004.01312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

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

- [A survey on secure decentralized optimization and learning](https://mlsanity.com/paper/2408.08628/) (2024)
- [Privacy-Preserving Distributed Optimization and Learning](https://mlsanity.com/paper/2403.00157/) (2024)

## Abstract (arXiv)

We present a distributed optimization protocol that preserves statistical privacy of agents' local cost functions against a passive adversary that corrupts some agents in the network. The protocol is a composition of a distributed ``em zero-sum" obfuscation protocol that obfuscates the agents' local cost functions, and a standard non-private distributed optimization method. We show that our protocol protects the statistical privacy of the agents' local cost functions against a passive adversary that corrupts up to t arbitrary agents as long as the communication network has (t+1)-vertex connectivity. The ``em zero-sum" obfuscation protocol preserves the sum of the agents' local cost functions and therefore ensures accuracy of the computed solution.