Adil Ahmad (Purdue University), Juhee Kim (Seoul National University), Jaebaek Seo (Google), Insik Shin (KAIST), Pedro Fonseca (Purdue University), Byoungyoung Lee (Seoul National University)

Intel SGX aims to provide the confidentiality of user data on untrusted cloud machines. However, applications that process confidential user data may contain bugs that leak information or be programmed maliciously to collect user data. Existing research that attempts to solve this problem does not consider multi-client isolation in a single enclave. We show that by not supporting such isolation, they incur considerable slowdown when concurrently processing multiple clients in different processes, due to the limitations of SGX.

This paper proposes CHANCEL, a sandbox designed for multi-client isolation within a single SGX enclave. In particular, CHANCEL allows a program’s threads to access both a per-thread memory region and a shared read-only memory region while servicing requests. Each thread handles requests from a single client at a time and is isolated from other threads, using a Multi-Client Software Fault Isolation (MCSFI) scheme. Furthermore, CHANCEL supports various in-enclave services such as an in-memory file system and shielded client communication to ensure complete mediation of the program’s interactions with the outside world. We implemented CHANCEL and evaluated it on SGX hardware using both micro-benchmarks and realistic target scenarios, including private information retrieval and product recommendation services. Our results show that CHANCEL outperforms a baseline multi-process sandbox between 4.06−53.70× on micro-benchmarks and 0.02 − 21.18× on realistic workloads while providing strong security guarantees.

View More Papers

RandRunner: Distributed Randomness from Trapdoor VDFs with Strong Uniqueness

Philipp Schindler (SBA Research), Aljosha Judmayer (SBA Research), Markus Hittmeir (SBA Research), Nicholas Stifter (SBA Research, TU Wien), Edgar Weippl (Universität Wien)

Read More

Data Poisoning Attacks to Deep Learning Based Recommender Systems

Hai Huang (Tsinghua University), Jiaming Mu (Tsinghua University), Neil Zhenqiang Gong (Duke University), Qi Li (Tsinghua University), Bin Liu (West Virginia University), Mingwei Xu (Tsinghua University)

Read More

Measuring DoT/DoH Blocking Using OONI Probe: a Preliminary Study

S. Basso (Open Observatory of Network Interference)

Read More

As Strong As Its Weakest Link: How to Break...

Kai Li (Syracuse University), Jiaqi Chen (Syracuse University), Xianghong Liu (Syracuse University), Yuzhe Tang (Syracuse University), XiaoFeng Wang (Indiana University Bloomington), Xiapu Luo (Hong Kong Polytechnic University)

Read More