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过程中，决策者依赖社交网络数据瞄准少数关键个体，通过利用同伴效应和社会传染机制最大化全民福利。开发兼顾隐私保护的社交网络数据采集与靶向干预新方法，对构建可持续的公共卫生及发展干预体系至关重要。与此类似，社交媒体平台依赖网络数据与历史传播信息组织广告投放，提升定向广告效能。确保这些网络操作不侵犯用户隐私，对于维持社交媒体平台及其广告经济的可持续发展具有关键意义。本文研究当社交网络未知、输入为随机采集的过往影响力传播样本时，影响力最大化算法的隐私保障机制。基于近期关于高成本网络信息播种策略的研究成果，我们的隐私保护算法通过在采集数据或算法输出中引入随机化，能够限定每个节点（或节点群组）在决定是否将其数据纳入算法输入时的隐私损失。我们在中央化与本地化两种隐私保护框架下，提供了有限样本量需满足差分隐私预算时的播种性能理论保障。基于合成网络与实证网络数据集的仿真实验揭示，随着隐私预算的降低，网络信息的价值在两种隐私保护框架下均呈现递减趋势。