CRII: SaTC: The Right to be Forgotten in Follow-ups of Machine Learning: When Privacy Meets Explanation and Efficiency

从各种来源收集的大量用户数据导致了机器学习的进步，并在许多现实世界的应用中采用了这些机器学习模型。然而，用户数据往往高度敏感，未经授权的数据泄露引发了人们对隐私的日益担忧。作为回应，最近的法规迫使组织允许用户主动从系统中删除他们的数据，以加强隐私保护。然而，从机器学习模型和系统中删除数据绝非易事。此外，法律学者批评继续使用针对删除的数据实例训练的机器学习模型，因为这侵犯了隐私。该项目对在机器学习模型的背景下从理论和经验上理解隐私泄露风险做出了重要贡献。该项目更广泛的意义和重要性在于，开发的算法保证了用户有权在社交媒体、医疗保健、金融等系统中完全删除他们的数据和数据的影响。该项目解决了与机器遗忘相关的新研究问题。研究了(1)模型解释、(2)模型剪枝和(3)迁移学习在用户数据被删除和机器遗忘发生时的隐私泄露风险和有效性。该项目还解决了后续任务，即设计遗忘算法，以消除解释、修剪模型和传递知识时忘记数据的影响。频繁的删除请求往往会带来昂贵的代价，现有的近似遗忘算法不能应用于解释、剪枝和迁移学习的制定。研究人员正在开发有效的移除算法，以执行忘记解释、修剪结构和迁移学习上的数据的更新步骤。该项目还具有一条管道，用于处理高风险域中频繁的数据删除请求，在这些域中，用户信息特别敏感。通过这些贡献，该项目将促进关于被遗忘权主题的研究进展，并缓解用户对机器学习中隐私的担忧。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The larger volume of user data collected from various sources has led to the advancement of machine learning and the adoption of these machine learning models in many real-world applications. However, user data is often highly sensitive, and unauthorized data releases have sparked increased concerns about privacy. In response, recent regulations compel organizations to allow users to proactively remove their data from a system for increased privacy protection. However, deleting data from machine-learning models and systems is non-trivial. Further, law scholars have criticized the continued use of machine learning models trained on deleted data instances as it violates privacy. This project significantly contributes to theoretically and empirically understanding the risk of privacy leaks in the context of machine learning models. The project's broader significance and importance are that the developed algorithms guarantee users the right to have their data and the influence of data completely deleted in systems such as social media, healthcare, finance, etc. This project addresses new research problems related to machine unlearning. It investigates privacy leakage risk and validity of (1) model explanation, (2) model pruning, and (3) transfer learning when user data has been deleted and machine unlearning happens. This project also tackles the subsequent tasks of designing unlearning algorithms to eliminate the influence of forgotten data from explanations, pruning models, and transferring knowledge. Frequent deletion requests often encounter expensive costs, and existing approximate unlearning algorithms cannot be applied to the formulation of explanation, pruning, and transfer learning. The investigator is developing efficient removal algorithms to perform an update step of forgetting data on the explanation, pruned structure, and transfer learning. This project also features a pipeline to handle frequent data deletion requests in high-stakes domains where user information is especially sensitive. With these contributions, the project will catalyze research progress on the topic of the right to be forgotten and ease users’ concerns about privacy in machine learning.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.