CAREER: New Frontiers in Computing on Private Data

职业：私有数据计算的新领域

• 批准号: 1942789
• 负责人: Abhishek Jain
• 金额: $ 57.64万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942789&HistoricalAwards=false
• 关键词: CAREER; New; Frontiers; Computing; Private

The ability to collect and analyze datasets has tremendous benefits to society. However, such benefits must not come at the price of privacy. This calls for secure and efficient methods for computing on private data that can resolve the conflict between the benefits of computation and the risk of privacy loss.The powerful paradigm of secure multiparty computation (MPC) can enable computation over datasets of mutually distrusting individuals and organizations while still preserving their privacy. Significant research advances in recent years have brought MPC closer than ever to practice. Nevertheless, many existing and emerging applications demand new efficiency and resiliency properties that are outside the reach of known solutions. To meet these demands, this project initiates two new lines of research in the study of MPC. First, to answer calls for MPC-as-a-service and MPC deployments in volunteer-based networks, this project develops new methods to enable large-scale computations. Second, to improve the resilience of MPC in strongly adversarial environments, this project develops new models and techniques to prevent data exfiltration even in the face of tampering attacks over protocol communication and computation. The project has outreach activities to raise awareness about security and cryptography in the greater Baltimore area.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.

收集和分析数据集的能力对社会有巨大的好处。然而，这些好处绝不能以隐私为代价。这就需要一种安全高效的私有数据计算方法来解决计算利益与隐私损失风险之间的冲突，安全多方计算（MPC）的强大范例可以在保护隐私的同时，在互不信任的个人和组织的数据集上进行计算。 近年来的重大研究进展使MPC比以往任何时候都更接近实践。然而，许多现有的和新兴的应用需要新的效率和弹性特性，这超出了已知解决方案的范围。为了满足这些需求，本项目在MPC的研究中启动了两条新的研究路线。首先，为了响应志愿者网络中MPC即服务和MPC部署的需求，该项目开发了新的方法来实现大规模计算。其次，为了提高MPC在强对抗环境中的弹性，该项目开发了新的模型和技术，即使在协议通信和计算上面临篡改攻击时也能防止数据泄漏。该项目有推广活动，以提高对安全和密码学的认识，在更大的巴尔的摩地区。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

On Actively-Secure Elementary MPC Reductions

关于主动安全的基本 MPC 缩减

• DOI: 10.1007/978-3-030-90459-3_24
• 发表时间: 2021
• 期刊: Theory of Cryptography 2021
• 作者: Benny Applebaum, Aarushi Goel

Time-Deniable Signatures

时效性签名

• 发表时间: 2022
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Beck, Gabrielle;Choudhuri, Arka Ray;Green, Matthew;Jain, Abhishek;Tiwari, Pratyush Ranjan
• 通讯作者: Tiwari, Pratyush Ranjan

Stacking Sigmas: A Framework to Compose Σ-Protocols for Disjunctions

• 发表时间: 2021
• 期刊: IACR Cryptol. ePrint Arch.
• 作者: Aarushi Goel;M. Green;Mathias Hall-Andersen;Gabriel Kaptchuk

Efficient Set Membership Proofs using MPC-in-the-Head

• DOI: 10.2478/popets-2022-0047
• 发表时间: 2022-03
• 期刊: Proceedings on Privacy Enhancing Technologies
• 作者: Aarushi Goel;M. Green;Mathias Hall-Andersen;Gabriel Kaptchuk

Threshold Signatures in the Multiverse

多元宇宙中的阈值签名

• 发表时间: 2023
• 期刊: 2023 IEEE Symposium on Security and Privacy (SP
• 作者: Baird, Leemon;Garg, Sanjam;Jain, Abhishek;Mukherjee, Pratyay;Sinha, Rohit;Wang, Minguan;Zhang, Yinuo
• 通讯作者: Zhang, Yinuo