2023/05/26 by Yuxin Liu, Liu, Yuxin, M. Amin Rahimian +3 · 1 voice · 1 citation
Computer Science · Mathematics · Social Sciences · #05C80 #91D30 #Applications (stat.AP) #Computational Complexity (cs.CC) #FOS: Computer and information sciences #FOS: Mathematics #Human Mobility and Location-Based Analysis #Multiagent Systems (cs.MA) #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Probability (math.PR) #Social and Information Networks (cs.SI) #cs.CC #cs.MA #cs.SI #math.PR #stat.AP
paper · pdf · doi:10.48550/arxiv.2305.16590
openalex publication_date 2023/05/26 · arxiv published 2023/05/26 · openalex created_date 2025/10/10 · arxiv updated 2026/06/19 · openalex updated_date 2026/08/01
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.