CRII: SaTC: Toward Secure, Privacy-Preserving, and Efficient Crowdsourcing Systems

研究人员和业界广泛使用众包系统在各个学科进行大规模数据收集和分析，以进行用户研究，提高机器学习性能，加速产品迭代。然而，今天的众包系统不能充分支持请求者和工作者，主要有三个问题：数据质量或完整性不足，侵犯隐私，任务完成效率低。本项目旨在解决这些问题，以提高众包系统的安全性、隐私性和效率。该项目的创新之处在于开发一种先进的质量控制方法来提高数据质量和完整性，一种新方案来检测和防止隐私侵犯，以及一个智能框架来提高效率。该项目更广泛的意义包括：(1)解决构建安全、隐私保护和高效的众包系统的关键挑战；(2)支持众包工作者，包括保护他们的隐私并帮助他们以经济高效的方式工作；(3)帮助工作申请者获得高质量的数据，他们可以依靠这些数据来完成重要的研究并自信地做出重要决策。该项目还有助于增加本科生对研究的参与，包括将研究成果纳入课程，并指导本科生进行研究。该项目增强了众包系统的安全性、隐私性和效率。首先，该项目开发了一种子任务感知和健壮的质量控制方法，以确保数据质量和完整性。其次，该项目调查了现实世界众包任务中工人和第三方的隐私风险，并设计了基于机器学习的方案来检测和防止隐私侵犯。第三，该项目设计并实施了一个基于人工智能技术的框架，以帮助工人更有效地识别和优先处理众包任务。该项目还将上述方案集成到一个工作人员可以使用的客户端浏览器扩展和一个可以应用于大规模部署的众包系统的服务器端模型中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Researchers and the industry have widely used crowdsourcing systems in various disciplines for large-scale data collection and analysis to conduct user studies, improve machine learning performance, and accelerate product iteration. However, today's crowdsourcing systems cannot adequately support requesters and workers due to three main problems: inadequate quality or integrity of data, privacy violations, and low efficiency of task completion. This project aims to address these problems to enhance security, privacy, and efficiency in crowdsourcing systems. The project's novelties are developing an advanced quality control approach to increase data quality and integrity, a novel scheme to detect and prevent privacy violations, and an intelligent framework to improve efficiency. The project's broader significance includes: (1) addressing the critical challenges of building secure, privacy-preserving, and efficient crowdsourcing systems, (2) supporting crowd workers, including protecting their privacy and assisting them to work in a cost-efficient way, and (3) assisting job requesters in obtaining high-quality data that they can rely on to complete their important studies and make important decisions confidently. The project also contributes to increasing undergraduate involvement in research, including integrating research outcomes into the curriculum and mentoring undergraduate students in conducting research. The project enhances security, privacy, and efficiency in crowdsourcing systems. First, the project develops a subtask-aware and robust quality control approach to ensure data quality and integrity. Second, the project investigates privacy risks to workers and third parties in real-world crowdsourcing tasks and designs machine learning-based schemes to detect and prevent privacy violations. Third, the project designs and implements a framework based on artificial intelligence techniques to assist workers in identifying and prioritizing crowdsourcing tasks more efficiently. This project also integrates the abovementioned schemes into a client-side browser extension that workers can use and a server-side model that can be applied in crowdsourcing systems for large-scale deployment.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.