# Protect Your Score: Contact Tracing With Differential Privacy Guarantees

arXiv: https://arxiv.org/abs/2312.11581 · HTML: https://mlsanity.com/paper/2312.11581/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo](https://mlsanity.com/paper/1502.07645/) (2015)
- [Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy](https://mlsanity.com/paper/2009.06389/) (2020)
- [Differential Privacy: An Economic Method for Choosing Epsilon](https://mlsanity.com/paper/1402.3329/) (2014)
- [On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis](https://mlsanity.com/paper/1603.07294/) (2016)
- [A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning](https://mlsanity.com/paper/2206.04621/) (2022)
- [Differentially Private Markov Chain Monte Carlo](https://mlsanity.com/paper/1901.10275/) (2019)
- [Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs](https://mlsanity.com/paper/2003.11511/) (2020)
- [A Survey of COVID-19 Contact Tracing Apps](https://mlsanity.com/paper/2006.10306/) (2020)
- [Decentralized Privacy-Preserving Proximity Tracing](https://mlsanity.com/paper/2005.12273/) (2020)
- [CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data](https://mlsanity.com/paper/2006.04942/) (2020)
- [Epidemic mitigation by statistical inference from contact tracing data](https://mlsanity.com/paper/2009.09422/) (2020)
- [COVI White Paper](https://mlsanity.com/paper/2005.08502/) (2020)
- [PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing](https://mlsanity.com/paper/2004.03544/) (2020)
- [Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic](https://mlsanity.com/paper/2003.08567/) (2020)
- [Considerations on the Theory of Training Models with Differential Privacy](https://mlsanity.com/paper/2303.04676/) (2023)
- [Cross Hashing: Anonymizing encounters in Decentralised Contact Tracing Protocols](https://mlsanity.com/paper/2005.12884/) (2020)

## Built on by (most cited first, 5 of 5)

- [Fast John Ellipsoid Computation with Differential Privacy Optimization](https://mlsanity.com/paper/2408.06395/) (2024)
- [FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health](https://mlsanity.com/paper/2509.14275/) (2025)
- [SoK: Security and Privacy Risks of Healthcare AI](https://mlsanity.com/paper/2409.07415/) (2024)
- [DNA: Differentially private Neural Augmentation for contact tracing](https://mlsanity.com/paper/2404.13381/) (2024)
- [Metric Embedding Initialization-Based Differentially Private and Explainable Graph Clustering](https://mlsanity.com/paper/2509.06214/) (2025)

## Abstract (arXiv)

The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in the early containment of the virus. While large strides have been made towards more effective contact tracing algorithms, we argue that privacy concerns currently hold deployment back. The essence of a contact tracing algorithm constitutes the communication of a risk score. Yet, it is precisely the communication and release of this score to a user that an adversary can leverage to gauge the private health status of an individual. We pinpoint a realistic attack scenario and propose a contact tracing algorithm with differential privacy guarantees against this attack. The algorithm is tested on the two most widely used agent-based COVID19 simulators and demonstrates superior performance in a wide range of settings. Especially for realistic test scenarios and while releasing each risk score with epsilon=1 differential privacy, we achieve a two to ten-fold reduction in the infection rate of the virus. To the best of our knowledge, this presents the first contact tracing algorithm with differential privacy guarantees when revealing risk scores for COVID19.