Min Hong Yun (Rice University), Lin Zhong (Rice University)

Many mobile and embedded apps possess sensitive data, or secrets. Trusting the operating system (OS), they often keep their secrets in the memory. Recent incidents have shown that the memory is not necessarily secure because the OS can be compromised due to inevitable vulnerabilities resulting from its sheer size and complexity. Existing solutions protect sensitive data against an untrusted OS by running app logic in the Secure world, a Trusted Execution Environment (TEE) supported by the ARM TrustZone technology. Because app logic increases the attack surface of their TEE, these solutions do not work for third-party apps.

This work aims to support third-party apps without growing the attack surface, significant development effort, or performance overhead. Our solution, called Ginseng, protects sensitive data by allocating them to registers at compile time and encrypting them at runtime before they enter the memory, due to function calls, exceptions or lack of physical registers. Ginseng does not run any app logic in the TEE and only requires minor markups to support existing apps. We report a prototype implementation based on LLVM, ARM Trusted Firmware (ATF), and the HiKey board. We evaluate it with both microbenchmarks and real-world secret-holding apps.

Our evaluation shows Ginseng efficiently protects sensitive data with low engineering effort. For example, a Ginseng-enabled web server, Nginx, protects the TLS master key with no measurable overhead. We find Ginseng's overhead is proportional to how often sensitive data in registers have to be encrypted and decrypted, i.e., spilling and restoring sensitive data on a function call or under high register pressure. As a result, Ginseng is most suited to protecting small sensitive data, like a password or social security number.

View More Papers

Analyzing Semantic Correctness with Symbolic Execution: A Case Study...

Sze Yiu Chau (Purdue University), Moosa Yahyazadeh (The University of Iowa), Omar Chowdhury (The University of Iowa), Aniket Kate (Purdue University), Ninghui Li (Purdue University)

Read More

Practical Hidden Voice Attacks against Speech and Speaker Recognition...

Hadi Abdullah (University of Florida), Washington Garcia (University of Florida), Christian Peeters (University of Florida), Patrick Traynor (University of Florida), Kevin R. B. Butler (University of Florida), Joseph Wilson (University of Florida)

Read More

Unveiling your keystrokes: A Cache-based Side-channel Attack on Graphics...

Daimeng Wang (University of California Riverside), Ajaya Neupane (University of California Riverside), Zhiyun Qian (University of California Riverside), Nael Abu-Ghazaleh (University of California Riverside), Srikanth V. Krishnamurthy (University of California Riverside), Edward J. M. Colbert (Virginia Tech), Paul Yu (U.S. Army Research Lab (ARL))

Read More

Quantity vs. Quality: Evaluating User Interest Profiles Using Ad...

Muhammad Ahmad Bashir (Northeastern University), Umar Farooq (LUMS Pakistan), Maryam Shahid (LUMS Pakistan), Muhammad Fareed Zaffar (LUMS Pakistan), Christo Wilson (Northeastern University)

Read More