Collaborative Research: SaTC: CORE: Small: Towards a Privacy-Preserving Framework for Research on Private, Encrypted Social Networks

协作研究：SaTC：核心：小型：针对私有加密社交网络研究的隐私保护框架

• 批准号: 2318844
• 负责人: Mohammad Amin Rahimian
• 金额: $ 20.8万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318844&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

This research aims to explore the role of private, encrypted social networks (ESNs) such as WhatsApp and iMessage in the spread of information and rumors. It seeks to understand how manipulated information is exposed, believed, and shared on ESNs compared to public social media platforms. By utilizing a privacy-preserving data donation framework, the project will develop tools to uncover and analyze the spread of manipulated information on encrypted platforms. The resulting data and software will be shared while ensuring data privacy and confidentiality. The research will shed light on how bad actors exploit unmoderated spaces to disseminate disinformation, particularly targeting vulnerable communities providing insights into the role of different social networks (public, semi-public, private) in spreading and influencing beliefs. This research will benefit various disciplines such as data science, journalism, cybersecurity, demography, social psychology, behavioral science, communications, and epidemiology. The tools developed can assist domain experts, policymakers, journalists, security operators, and NGOs in supporting at-risk populations and devising effective solutions. Ultimately, the research aims to empower ESN users by providing information about their information consumption and identifying instances of rumor and inauthentic information.In this work, using data collected with a novel privacy-preserving data donation framework, the project team studies the exposure, belief in and sharing of information on encrypted social networks versus public social media platforms. The technical contributions would enable: a large-scale, anonymized data collection framework to identify viral content spreading on encrypted platforms in a privacy-preserving manner; and statistical measures of the prevalence and incidence of viral rumor and hearsay in the target population using the collected data, while controlling the privacy risks to our users from publishing statistics based on their donated data and survey responses. The project team is developing novel algorithms that work on private and public data and come with tunable parameters that allow the researchers to balance the loss in statistical efficiency from randomization against a differential privacy budget. The statistical measures proposed allow working with private ESN data and sharing those answers without undue risk to the privacy of data donors. Simulation studies with calibrated measures of spread allow the team to evaluate the large-scale impact of various platform strategies across public and private social networks, and experimental designs with human subjects are used to evaluate the impact of potential interventions on individuals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究旨在探讨 WhatsApp 和 iMessage 等私人加密社交网络 (ESN) 在信息和谣言传播中的作用。它旨在了解与公共社交媒体平台相比，ESN 上如何暴露、相信和共享受操纵的信息。通过利用保护隐私的数据捐赠框架，该项目将开发工具来发现和分析加密平台上被操纵信息的传播。由此产生的数据和软件将被共享，同时确保数据隐私和机密性。该研究将揭示不良行为者如何利用不受控制的空间传播虚假信息，特别是针对弱势社区，从而深入了解不同社交网络（公共、半公共、私人）在传播和影响信仰方面的作用。这项研究将惠及数据科学、新闻学、网络安全、人口学、社会心理学、行为科学、传播学和流行病学等多个学科。开发的工具可以帮助领域专家、政策制定者、记者、安全操作员和非政府组织支持高危人群并制定有效的解决方案。最终，该研究旨在通过提供有关 ESN 用户信息消费的信息并识别谣言和不真实信息的实例来增强 ESN 用户的能力。在这项工作中，项目团队使用通过新颖的隐私保护数据捐赠框架收集的数据来研究曝光度、信念与公共社交媒体平台相比，在加密社交网络上进行信息共享。技术贡献将实现： 一个大规模的匿名数据收集框架，以保护隐私的方式识别在加密平台上传播的病毒内容；使用收集的数据对目标人群中病毒性谣言和传闻的流行率和发生率进行统计测量，同时根据用户捐赠的数据和调查回复发布统计数据来控制用户的隐私风险。该项目团队正在开发适用于私人和公共数据的新颖算法，并带有可调参数，使研究人员能够平衡随机化造成的统计效率损失与差异化隐私预算。拟议的统计措施允许使用私人 ESN 数据并共享这些答案，而不会对数据捐赠者的隐私造成不当风险。通过校准传播测量的模拟研究，团队可以评估公共和私人社交网络中各种平台策略的大规模影响，并使用人类受试者的实验设计来评估潜在干预措施对个人的影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。