CRII: SaTC: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。在云计算中，公共云服务提供商可以提供云存储作为主要服务，同时通过使用存储中的客户数据提供额外的基于机器学习(ML)的服务。尽管这种商业模式前景看好，但它也带来了安全担忧，因为公共商业云不能完全信任。例如，公共商业云可能会将客户的敏感数据出售给第三方。在这个项目中，研究人员开发了一个方案，在云存储中的加密数据库上实现安全和隐私保护的机器学习服务。该项目的更广泛的意义和重要性是双重的。首先，该项目扩展了云计算服务的边界，并带来了新的业务增长可能性，使存储在公共云中的数据能够进行更安全和保护隐私的人工智能分析。其次，该项目吸引了女性和代表性不足的少数族裔学生从事计算机工作，从而培养了21世纪有数据能力的劳动力。在这个项目中，研究人员开发了一种方案，通过使用索引辅助方法来支持云存储中加密数据库的安全ML查询。在该方法中，使用AES/DES对数据集中的每个数据项(即训练样本)进行形式加密，以实现强密文保密。对于每个数据项，生成能够支持ML分析的安全索引项。因此，该方案同时实现了较强的密文保密性和ML能力，以及索引、模型和令牌保密性。总而言之，该项目做出了两个重大的技术贡献。首先，开发一种索引辅助方法，以解决数据安全和效用之间的冲突。第二，开发人工智能驱动的数据加密/掩码技术，在隐私和运行时性能方面优于差异隐私和同态加密。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).In cloud computing, public cloud service providers can provide cloud storage as the primary service, while providing additional machine learning (ML)-based services by using the clients' data in storage. Although this business model is promising, it also brings in security concerns since the public commercial cloud cannot be fully trusted. For example, public commercial clouds may sell clients' sensitive data to third parties. In this project, the investigator develops a scheme to enable secure and privacy-preserving machine learning services over the encrypted database in cloud storage. The project's broader significance and importance are two-fold. First, the project extends the border of cloud computing services and brings in new business growth possibilities that enable safer and privacy-preserving AI analyses on data stored in public clouds. Second, the project engages female and under-represented minority students in computing, thus fostering the 21st-century data-capable workforce. In this project, the investigator develops a scheme to support secure ML queries over encrypted databases in cloud storage by employing an index-aid approach. In this approach, each data item (i.e., a training example) in a dataset is formally encrypted using AES/DES to achieve strong ciphertext privacy. For each data item, a secure index item that can support ML analyses is generated. Therefore, this scheme achieves strong ciphertext privacy and ML capability simultaneously, as well as index, model, and token privacy. In summary, this project makes two significant technical contributions. First, the development of an index-aid approach that addresses the conflict between data security and utility. Second, the development of AI-driven data encryption/mask techniques that outperform differential privacy and homomorphic encryption regarding privacy and runtime performance.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.