Differentially Private Seeding Algorithms for Public Health Interventions Using Contact Tracing Data
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[Submitted on 26 May 2023 (v1), last revised 19 Jun 2026 (this version, v7)]
Summary
This paper addresses the challenge of using seeding algorithms for public health interventions (like PrEP distribution for HIV prevention) when complete sexual activity networks are unavailable due to privacy concerns. The authors propose differential privacy-preserving algorithms that work with influence samples from contact tracing rather than full network data. They introduce randomization in data or outputs to bound individual privacy loss, and demonstrate through theoretical analysis and simulations on synthetic and real-world sexual contact data that performance degrades gracefully as privacy budgets tighten, with central privacy regimes outperforming local ones.
Source
bskyDifferentially Private Seeding Algorithms for Public Health Interventions Using Contact Tracing Dataarxiv.orgKey quotes
· 4 pulledIn public health interventions such as distributing preexposure prophylaxis (PrEP) for HIV prevention, decision makers often use seeding algorithms to identify key individuals who can amplify intervention impact.
Building a complete sexual activity network is typically infeasible due to privacy concerns.
We study differential privacy guarantees for influence maximization when the input consists of randomly collected cascades.
Theoretical analysis and simulations on synthetic and real-world sexual contact data show that performance degrades gracefully as privacy budgets tighten, with central privacy regimes achieving better trade-offs than local ones.
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