Seeding with Differentially Private Network Information

TL;DR

This paper develops privacy-preserving seeding algorithms for influence maximization in incomplete sexual contact networks, balancing privacy guarantees with influence spread effectiveness.

Contribution

It introduces differential privacy methods tailored for influence maximization from contact tracing data, addressing privacy concerns in network-based health interventions.

Findings

Performance degrades gracefully with tighter privacy budgets.

Central privacy regimes outperform local privacy in trade-offs.

Algorithms effectively protect individual privacy while maintaining influence spread.

Abstract

In 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. However, building a complete sexual activity network is typically infeasible due to privacy concerns. Instead, contact tracing can provide influence samples, observed sequences of sexual contacts, without full network reconstruction. This raises two challenges: protecting individual privacy in these samples and adapting seeding algorithms to incomplete data. We study differential privacy guarantees for influence maximization when the input consists of randomly collected cascades. Building on recent advances in costly seeding, we propose privacy-preserving algorithms that introduce randomization in data or outputs and bound the privacy loss of each node. 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.