When designing interventions in public health, development, and education, decision makers rely on social network data to target a small number of people, capitalizing on peer effects and social contagion to bring about the most welfare benefits to the population. Developing new methods that are privacy-preserving for network data collection and targeted interventions is critical for designing sustainable public health and development interventions on social networks. In a similar vein, social media platforms rely on network data and information from past diffusions to organize their ad campaign and improve the efficacy of targeted advertising. Ensuring that these network operations do not violate users' privacy is critical to the sustainability of social media platforms and their ad economies. We study privacy guarantees for influence maximization algorithms when the social network is unknown, and the inputs are samples of prior influence cascades that are collected at random. Building on recent results that address seeding with costly network information, our privacy-preserving algorithms introduce randomization in the collected data or the algorithm output, and can bound each node's (or group of nodes') privacy loss in deciding whether or not their data should be included in the algorithm input. We provide theoretical guarantees of the seeding performance with a limited sample size subject to differential privacy budgets in both central and local privacy regimes. Simulations on synthetic and empirical network datasets reveal the diminishing value of network information with decreasing privacy budget in both regimes.

翻译：在公共卫生、发展及教育领域的干预措施设计中，决策者依赖于社交网络数据来精准定位少数人群，利用同伴效应和社会传染来为人口带来最大的福利收益。开发保护网络数据收集和定向干预隐私的新方法，对于设计社交网络上可持续的公共卫生和发展干预措施至关重要。类似地，社交媒体平台依赖网络数据及过往传播信息来组织广告活动，提升定向广告的效果。确保这些网络操作不侵犯用户隐私，对社交媒体平台及其广告经济的可持续性至关重要。我们研究了当社交网络未知且输入为随机收集的先前影响级联样本时，影响力最大化算法的隐私保证。基于近期解决昂贵网络信息种子选择问题的成果，我们的隐私保护算法在收集数据或算法输出中引入随机化，并能约束每个节点（或节点组）在决定其数据是否应被纳入算法输入时的隐私损失。我们提供了在中央和本地两种隐私机制下，受差分隐私预算限制、样本量有限时种子选择性能的理论保证。在合成和实证网络数据集上的模拟揭示了，在两种机制中网络信息的价值均随隐私预算降低而递减。