Jim Alves-Foss, Varsha Venugopal (University of Idaho)

The effectiveness of binary analysis tools and techniques is often measured with respect to how well they map to a ground truth. We have found that not all ground truths are created equal. This paper challenges the binary analysis community to take a long look at the concept of ground truth, to ensure that we are in agreement with definition(s) of ground truth, so that we can be confident in the evaluation of tools and techniques. This becomes even more important as we move to trained machine learning models, which are only as useful as the validity of the ground truth in the training.

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ROV-MI: Large-Scale, Accurate and Efficient Measurement of ROV Deployment

Wenqi Chen (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Chenxin Duan (Tsinghua University), Xia Yin (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University)

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SynthCT: Towards Portable Constant-Time Code

Sushant Dinesh (University of Illinois at Urbana Champaign), Grant Garrett-Grossman (University of Illinois at Urbana Champaign), Christopher W. Fletcher (University of Illinois at Urbana Champaign)

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Property Inference Attacks Against GANs

Junhao Zhou (Xi'an Jiaotong University), Yufei Chen (Xi'an Jiaotong University), Chao Shen (Xi'an Jiaotong University), Yang Zhang (CISPA Helmholtz Center for Information Security)

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Symbolic Path Tracing to Find Android Permission-Use Triggers

Kristopher Micinski (Haverford College), Thomas Gilray (University of Alabama, Birmingham), Daniel Votipka (University of Maryland), Michelle L. Mazurek (University of Maryland), Jeffrey S. Foster (Tufts University)

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