# Customized Local Differential Privacy for Multi-Agent Distributed Optimization

arXiv: https://arxiv.org/abs/1806.06035 · HTML: https://mlsanity.com/paper/1806.06035/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses](https://mlsanity.com/paper/2106.09779/) (2021)
- [Private Non-Convex Federated Learning Without a Trusted Server](https://mlsanity.com/paper/2203.06735/) (2022)
- [On Differentially Private Federated Linear Contextual Bandits](https://mlsanity.com/paper/2302.13945/) (2023)

## Abstract (arXiv)

Real-time data-driven optimization and control problems over networks may require sensitive information of participating users to calculate solutions and decision variables, such as in traffic or energy systems. Adversaries with access to coordination signals may potentially decode information on individual users and put user privacy at risk. We develop local differential privacy, which is a strong notion that guarantees user privacy regardless of any auxiliary information an adversary may have, for a larger family of convex distributed optimization problems. The mechanism allows agent to customize their own privacy level based on local needs and parameter sensitivities. We propose a general sampling based approach for determining sensitivity and derive analytical bounds for specific quadratic problems. We analyze inherent trade-offs between privacy and suboptimality and propose allocation schemes to divide the maximum allowable noise, a privacy budget, among all participating agents. Our algorithm is implemented to enable privacy in distributed optimal power flow for electric grids.