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: 2318843
• 负责人: Venkata Rama Ki Garimella
• 金额: $ 27.2万
• 依托单位: Rutgers University New Brunswick
• 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=2318843&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.

这项研究旨在探索私人，加密的社交网络（ESN）的作用，例如WhatsApp和Imessage在信息和谣言的传播中。它试图了解与公共社交媒体平台相比，在ESN上如何暴露，相信和共享操纵信息。通过利用保护隐私的数据捐赠框架，该项目将开发工具来揭示和分析受操纵信息在加密平台上的传播。所得数据和软件将在确保数据隐私和机密性的同时共享。这项研究将阐明糟糕的参与者如何利用未经修改的空间来传播虚假信息，尤其是针对脆弱的社区，以洞悉不同社交网络（公共，半公共，私人）在传播和影响信念中的作用。这项研究将受益于各种学科，例如数据科学，新闻，网络安全，人口统计学，社会心理学，行为科学，沟通和流行病学。开发的工具可以帮助领域专家，决策者，记者，安全操作员和非政府组织提供支持，并为高危人群提供支持并设计有效的解决方案。最终，该研究旨在通过提供有关其信息消耗的信息并确定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，使用具有新型隐私数据捐赠框架收集的数据，该项目团队研究了在加密的社交网络与公共社交媒体平台上对信息的敞口，信念和共享。技术贡献将启用：一个大规模的匿名数据收集框架，以识别以隐私的方式在加密平台上传播的病毒内容；以及使用收集到的数据在目标人群中病毒谣言和传闻的流行率和发病率的统计量度，同时根据其捐赠数据和调查响应来控制我们用户从发布统计信息的隐私风险。项目团队正在开发新型算法，这些算法在私人和公共数据上工作，并带有可调参数，使研究人员能够平衡从随机分组中的统计效率损失与差异隐私预算的损失。拟议的统计措施允许使用私人ESN数据并共享这些答案，而不会对数据捐献者的隐私造成不适当的风险。通过校准量的衡量标准的仿真研究使团队能够评估各种平台策略在公共和私人社交网络中的大规模影响，并且使用人类受试者的实验设计用于评估潜在干预对个人的影响。该奖项反映了NSF的法规任务，并认为通过基金会的知识优点和广泛的criter scriter scriter scriter scriter criter criteria criter criteria criter criteria criter criter criteria criter criteria criter criteria均值得一评论。