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.

翻译：在公共卫生干预措施中，例如分发HIV暴露前预防（PrEP）药物，决策者通常采用种子选择算法来识别能够放大干预效果的关键个体。然而，由于隐私问题，构建完整的性活动网络通常不可行。相反，接触者追踪可以提供影响力样本，即观察到的性接触序列，而无需重建完整网络。这带来了两个挑战：保护这些样本中的个体隐私，以及使种子选择算法适应不完整数据。我们研究了当输入由随机收集的级联组成时，影响力最大化的差分隐私保证。基于近期代价种子选择的研究进展，我们提出了在数据或输出中引入随机化的隐私保护算法，并界定了每个节点的隐私损失。在合成和真实世界性接触数据上的理论分析和模拟表明，随着隐私预算收紧，性能下降平缓，且中心隐私机制比本地机制实现了更好的权衡。