Adil Ahmad (Purdue), Byunggill Joe (KAIST), Yuan Xiao (Ohio State University), Yinqian Zhang (Ohio State University), Insik Shin (KAIST), Byoungyoung Lee (Purdue/SNU)

Program obfuscation is a popular cryptographic construct with a wide range of uses such as IP theft prevention. Although cryptographic solutions for program obfuscation impose impractically high overheads, a recent breakthrough leveraging trusted hardware has shown promise. However, the existing solution is based on special-purpose trusted hardware, restricting its use-cases to a limited few.

In this paper, we first study if such obfuscation is feasible based on commodity trusted hardware, Intel SGX, and we observe that certain important security considerations are not afforded by commodity hardware. In particular, we found that existing obfuscation/obliviousness schemes are insecure if directly applied to Intel SGX primarily due to side-channel limitations. To this end, we present OBFUSCURO, the first system providing program obfuscation using commodity trusted hardware, Intel SGX. The key idea is to leverage ORAM operations to perform secure code execution and data access. Initially, OBFUSCURO transforms the regular program layout into a side-channel-secure and ORAM-compatible layout. Then, OBFUSCURO ensures that its ORAM controller performs data oblivious accesses in order to protect itself from all memory-based side-channels. Furthermore, OBFUSCURO ensures that the program is secure from timing attacks by ensuring that the program always runs for a pre-configured time interval. Along the way, OBFUSCURO also introduces a systematic optimization such as register-based ORAM stash. We provide a thorough security analysis of OBFUSCURO along with empirical attack evaluations showing that OBFUSCURO can protect the SGX program execution from being leaked by access pattern-based and timing-based channels. We also provide a detailed performance benchmark results in order to show the practical aspects of OBFUSCURO.

View More Papers

NIC: Detecting Adversarial Samples with Neural Network Invariant Checking

Shiqing Ma (Purdue University), Yingqi Liu (Purdue University), Guanhong Tao (Purdue University), Wen-Chuan Lee (Purdue University), Xiangyu Zhang (Purdue University)

Read More

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

ConcurORAM: High-Throughput Stateless Parallel Multi-Client ORAM

Anrin Chakraborti (Stony Brook University), Radu Sion (Stony Brook University)

Read More

Latex Gloves: Protecting Browser Extensions from Probing and Revelation...

Alexander Sjösten (Chalmers University of Technology), Steven Van Acker (Chalmers University of Technology), Pablo Picazo-Sanchez (Chalmers University of Technology), Andrei Sabelfeld (Chalmers University of Technology)

Read More