NSFSaTC-BSF: TWC: Small: Enabling Secure and Private Cloud Computing using Coresets

NFSaTC-BSF：TWC：小型：使用核心集实现安全和私有云计算

• 批准号: 1526815
• 负责人: Daniela Rus
• 金额: $ 48.61万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526815&HistoricalAwards=false
• 关键词: NSFSaTC; BSF; TWC; Small; Enabling

By collecting sensor data from individuals in a user community, e.g., using their smartphones, it is possible to learn the behavior of communities, for example locations, activities, and events. Similarly, using data from personal health monitoring sensors, it is possible to learn about the health risks and responses to treatments for population groups. But is it possible to use the valuable information for the greater good without disclosing information about the individuals contributing the data? What about protecting this information from improper access? This project uses cloud computing augmented with a combination of data reduction techniques and methods from differential privacy and homomorphic encryption to address such questions. The key ideas are (1) to use coresets as a way of mitigating the computational challenges around the state of the art in differential privacy and homomorphic encryption and to ensure private secure computation on the server side and the client side, and (2) to give the data owners control over setting access to their data as a trade-off between data access control guarantees and computation accuracy. Combining coresets, differential privacy, and homomorphic encryption has the potential for practical private and secure computation in the cloud.This proposal builds on previous results in coresets, private coresets, and their implementations for the cloud. Coresets are a data reduction technique for computing a function f on a large data set D efficiently by compressing the initial data into a small data set C (possibly on the cloud), and then solving the problem f on the reduced set C (now, possible also at the client). The reduced data set C is chosen so that it is fast to compute f(C) and f(C) ~ f(D). A particular type is coreset is the private coreset, which preserves privacy but must be constructed and sanitized on the client side. On the other hand, fully homomorphic encryption allows encrypted computation on the server side but it is usually impractical. This project develops (i) New private coresets for broad classes of practical problems, with focus on generic frameworks as for non-private coresets; (ii) Novel algorithms and techniques for efficient homomorphic encryption on the cloud using coresets; (iii) Private Encrypted Coresets which are new type of coresets that simultaneously preserve privacy and can be computed securely on the cloud; (iv) Life-logging systems that implement and combine the above techniques for simultaneous secure and private computation in the cloud, with appropriate benchmarks and real-world testing.

通过从用户社区中的个人收集传感器数据（例如使用他们的智能手机），可以了解社区的行为，例如位置、活动和事件。同样，使用个人健康监测传感器的数据，可以了解人群的健康风险和对治疗的反应。但是，是否有可能在不披露贡献数据的个人信息的情况下将有价值的信息用于更大的利益呢？如何保护这些信息免遭不当访问？该项目使用云计算，并结合数据缩减技术和差分隐私和同态加密的方法来解决此类问题。关键思想是（1）使用核心集作为缓解差分隐私和同态加密领域现有技术的计算挑战的一种方式，并确保服务器端和客户端的私有安全计算，以及（2）使数据所有者能够控制对其数据的访问设置，作为数据访问控制保证和计算准确性之间的权衡。 将核心集、差分隐私和同态加密相结合，有可能在云中实现实用的私有和安全计算。该提案建立在核心集、私有核心集及其云实现的先前结果的基础上。 Coresets 是一种数据缩减技术，用于通过将初始数据压缩为小数据集 C（可能在云端），然后在缩减集 C 上解决问题 f，从而在大数据集 D 上高效计算函数 f（现在，也可以在客户端）。选择缩减数据集 C，以便快速计算 f(C) 和 f(C) ~ f(D)。核心集的一种特殊类型是私有核心集，它保留隐私，但必须在客户端构建和清理。另一方面，完全同态加密允许在服务器端进行加密计算，但这通常是不切实际的。该项目开发（i）针对广泛的实际问题的新私有核心集，重点关注非私有核心集的通用框架； (ii) 使用核心集在云上实现高效同态加密的新算法和技术； (iii) 私有加密核心集，这是一种新型核心集，可以同时保护隐私并可以在云上安全计算； (iv) 生活记录系统，实施并结合上述技术，在云中同时进行安全和私密计算，并进行适当的基准测试和实际测试。