Caihua Li (Yale University), Seung-seob Lee (Yale University), Lin Zhong (Yale University)

Confidential Computing (CC) has received increasing attention in recent years as a mechanism to protect user data from untrusted operating systems (OSes). Existing CC solutions hide confidential memory from the OS and/or encrypt it to achieve confidentiality. In doing so, they render OS memory optimization unusable or complicate the trusted computing base (TCB) required for optimization. This paper presents our results toward overcoming these limitations, synthesized in a CC design named Blindfold. Like many other CC solutions, Blindfold relies on a small trusted software component running at a higher privilege level than the kernel, called Guardian. It features three techniques that can enhance existing CC solutions. First, instead of nesting page tables, Blindfold’s Guardian mediates how the OS accesses memory and handles exceptions by switching page and interrupt tables. Second, Blindfold employs a lightweight capability system to regulate the OS’s semantic access to user memory, unifying case-by-case approaches in previous work. Finally, Blindfold provides carefully designed secure ABI for confidential memory management without encryption. We report an implementation of Blindfold that works on ARMv8-A/Linux. Using Blindfold's prototype, we are able to evaluate the cost of enabling confidential memory management by the untrusted Linux kernel. We show Blindfold has a smaller runtime TCB than related systems and enjoys competitive performance. More importantly, we show that the Linux kernel, including all of its memory optimizations except memory compression, can function properly for confidential memory. This requires only about 400 lines of kernel modifications.

View More Papers

Misdirection of Trust: Demystifying the Abuse of Dedicated URL...

Zhibo Zhang (Fudan University), Lei Zhang (Fudan University), Zhangyue Zhang (Fudan University), Geng Hong (Fudan University), Yuan Zhang (Fudan University), Min Yang (Fudan University)

Read More

CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models

Rui Zeng (Zhejiang University), Xi Chen (Zhejiang University), Yuwen Pu (Zhejiang University), Xuhong Zhang (Zhejiang University), Tianyu Du (Zhejiang University), Shouling Ji (Zhejiang University)

Read More

Defending Against Membership Inference Attacks on Iteratively Pruned Deep...

Jing Shang (Beijing Jiaotong University), Jian Wang (Beijing Jiaotong University), Kailun Wang (Beijing Jiaotong University), Jiqiang Liu (Beijing Jiaotong University), Nan Jiang (Beijing University of Technology), Md Armanuzzaman (Northeastern University), Ziming Zhao (Northeastern University)

Read More