Hyun Bin Lee (University of Illinois at Urbana-Champaign), Tushar M. Jois (Johns Hopkins University), Christopher W. Fletcher (University of Illinois at Urbana-Champaign), Carl A. Gunter (University of Illinois at Urbana-Champaign)

Users can improve the security of remote communications by using Trusted Execution Environments (TEEs) to protect against direct introspection and tampering of sensitive data. This can even be done with applications coded in high-level languages with complex programming stacks such as R, Python, and Ruby. However, this creates a trade-off between programming convenience versus the risk of attacks using microarchitectural side channels.

In this paper, we argue that it is possible to address this problem for important applications by instrumenting a complex programming environment (like R) to produce a Data-Oblivious Transcript (DOT) that is explicitly designed to support computation that excludes side channels. Such a transcript is then evaluated on a Trusted Execution Environment (TEE) containing the sensitive data using a small trusted computing base called the Data-Oblivious Virtual Environment (DOVE).

To motivate the problem, we demonstrate a number of subtle side-channel vulnerabilities in the R language. We then provide an illustrative design and implementation of DOVE for R, creating the first side-channel resistant R programming stack. We demonstrate that the two-phase architecture provided by DOT generation and DOVE evaluation can provide practical support for complex programming languages with usable performance and high security assurances against side channels.

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From WHOIS to WHOWAS: A Large-Scale Measurement Study of...

Chaoyi Lu (Tsinghua University; Beijing National Research Center for Information Science and Technology), Baojun Liu (Tsinghua University; Beijing National Research Center for Information Science and Technology; Qi An Xin Group), Yiming Zhang (Tsinghua University; Beijing National Research Center for Information Science and Technology), Zhou Li (University of California, Irvine), Fenglu Zhang (Tsinghua University), Haixin Duan…

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More than a Fair Share: Network Data Remanence Attacks...

Leila Rashidi (University of Calgary), Daniel Kostecki (Northeastern University), Alexander James (University of Calgary), Anthony Peterson (Northeastern University), Majid Ghaderi (University of Calgary), Samuel Jero (MIT Lincoln Laboratory), Cristina Nita-Rotaru (Northeastern University), Hamed Okhravi (MIT Lincoln Laboratory), Reihaneh Safavi-Naini (University of Calgary)

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UIScope: Accurate, Instrumentation-free, and Visible Attack Investigation for GUI...

Runqing Yang (Zhejiang University), Shiqing Ma (Rutgers University), Haitao Xu (Arizona State University), Xiangyu Zhang (Purdue University), Yan Chen (Northwestern University)

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EarArray: Defending against DolphinAttack via Acoustic Attenuation

Guoming Zhang (Zhejiang University), Xiaoyu Ji (Zhejiang University), Xinfeng Li (Zhejiang University), Gang Qu (University of Maryland), Wenyuan Xu (Zhejing University)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)