CAREER: Unified Reference-Free Early Detection of Hardware Trojans via Knowledge Graph Embeddings

职业：通过知识图嵌入对硬件木马进行统一的无参考早期检测

• 批准号: 2238976
• 负责人: Inna Partin-Vaisband
• 金额: $ 60万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238976&HistoricalAwards=false
• 关键词: CAREER; Unified; Reference; Free; Early

In recent years, cloud computing has risen to unparalleled societal importance, as it enables a broad range of applications, including healthcare services, social media, banking and electronic commerce. As a result, cloud services have access to the digital data of millions of users and become a lucrative target for criminals and cyberattacks. Sensitive data are stolen from cloud servers, incurring significant financial losses to those impacted. To address the shortcomings of current protection technologies in cloud computing, this project employs advanced cryptography and introduces new privacy protections that allow data to remain secure even when the cloud service is compromised. The project's novelties are in the judicious design of computers that can understand and process encrypted information such that, even if data are stolen or under attack, they will be incomprehensible to the attacker. The project's broader significance and importance is in addressing the urgent need of the cloud computing industry to mitigate the impact of cyberattacks and to protect the privacy of users, which is aligned with the mission of the National Science Foundation. The key observation in this project is that data are immune to leakage threats as long as they remain encrypted at all times, including during processing. Towards that end, the investigator employs a special form of encryption, called Fully Homomorphic Encryption, to protect general-purpose computation using diverse hardware platforms such as graphics processing units. Specifically, this project defines imperative models of computation that improve the programmability of encrypted data processing, without sacrificing execution performance. These models are complemented with real-life applications such as privacy-preserving machine learning and data mining that harness the power of encrypted computation. The results of this project are integrated with educational activities, including web-based learning environments and cybersecurity competitions.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.

近年来，云计算已经提高到无与伦比的社会重要性，因为它可以实现广泛的应用，包括医疗保健服务，社交媒体，银行业和电子商务。结果，云服务可以访问数百万用户的数字数据，并成为犯罪分子和网络攻击的有利可图的目标。敏感的数据是从云服务器中偷走的，这给受影响的数据带来了巨大的财务损失。为了解决云计算中当前保护技术的缺点，该项目采用了高级加密图，并引入了新的隐私保护措施，即使云服务受到损害，这些隐私保护也可以保持安全。该项目的新颖性在于可以理解和处理加密信息的计算机的明智设计，即使数据被盗或受到攻击，攻击者也无法理解。该项目的重要意义和重要性在于解决云计算行业的迫切需要减轻网络攻击的影响并保护用户的隐私，这与国家科学基金会的使命保持一致。该项目的主要观察结果是，只要数据始终保持加密，包括在处理过程中，数据可以免疫泄漏威胁。为此，研究人员采用了一种特殊的加密形式，称为完全同构加密，以使用各种硬件平台（例如图形处理单元）来保护通用计算。具体而言，该项目定义了命令的计算模型，以改善加密数据处理的可编程性，而无需牺牲执行绩效。这些模型与现实生活中的应用程序相辅相成，例如保护隐私的机器学习和数据挖掘，以利用加密计算的力量。该项目的结果与教育活动相结合，包括基于Web的学习环境和网络安全竞争。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。