# Differential Privacy on Trust Graphs

arXiv: https://arxiv.org/abs/2410.12045 · HTML: https://mlsanity.com/paper/2410.12045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 59 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Multi-scale Attributed Node Embedding](https://mlsanity.com/paper/1909.13021/) (2019)
- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity](https://mlsanity.com/paper/1811.12469/) (2018)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [Concentrated Differential Privacy](https://mlsanity.com/paper/1603.01887/) (2016)
- [Distributed Differential Privacy via Shuffling](https://mlsanity.com/paper/1808.01394/) (2018)
- [The Privacy Blanket of the Shuffle Model](https://mlsanity.com/paper/1903.02837/) (2019)
- [Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization](https://mlsanity.com/paper/1512.09170/) (2015)
- [Optimal Algorithms for Mean Estimation under Local Differential Privacy](https://mlsanity.com/paper/2205.02466/) (2022)
- [Private Summation in the Multi-Message Shuffle Model](https://mlsanity.com/paper/2002.00817/) (2020)
- [Privacy Amplification by Decentralization](https://mlsanity.com/paper/2012.05326/) (2020)
- [Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message](https://mlsanity.com/paper/2109.13158/) (2021)
- [Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead](https://mlsanity.com/paper/2106.04247/) (2021)
- [Private Aggregation from Fewer Anonymous Messages](https://mlsanity.com/paper/1909.11073/) (2019)
- [Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs](https://mlsanity.com/paper/2003.11511/) (2020)
- [Multi-Central Differential Privacy](https://mlsanity.com/paper/2009.05401/) (2020)
- [Dealing with Range Anxiety in Mean Estimation via Statistical Queries](https://mlsanity.com/paper/1611.06475/) (2016)
- [Necessary Conditions in Multi-Server Differential Privacy](https://mlsanity.com/paper/2208.08540/) (2022)
- [Differential Privacy of Populations in Routing Games](https://mlsanity.com/paper/1601.04041/) (2016)

## Abstract (arXiv)

We study differential privacy (DP) in a multi-party setting where each party only trusts a (known) subset of the other parties with its data. Specifically, given a trust graph where vertices correspond to parties and neighbors are mutually trusting, we give a DP algorithm for aggregation with a much better privacy-utility trade-off than in the well-studied local model of DP (where each party trusts no other party). We further study a robust variant where each party trusts all but an unknown subset of at most t of its neighbors (where t is a given parameter), and give an algorithm for this setting. We complement our algorithms with lower bounds, and discuss implications of our work to other tasks in private learning and analytics.