Jairo Giraldo (University of Utah), Alvaro Cardenas (UC Santa Cruz), Murat Kantarcioglu (UT Dallas), Jonathan Katz (George Mason University)

Differential Privacy has emerged in the last decade as a powerful tool to protect sensitive information. Similarly, the last decade has seen a growing interest in adversarial classification, where an attacker knows a classifier is trying to detect anomalies and the adversary attempts to design examples meant to mislead this classification.

Differential privacy and adversarial classification have been studied separately in the past. In this paper, we study the problem of how a strategic attacker can leverage differential privacy to inject false data in a system, and then we propose countermeasures against these novel attacks. We show the impact of our attacks and defenses in a real-world traffic estimation system and in a smart metering system.

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Broken Metre: Attacking Resource Metering in EVM

Daniel Perez (Imperial College London), Benjamin Livshits (Imperial College London, UCL Centre for Blockchain Technologies, and Brave Software)

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Automated Discovery of Cross-Plane Event-Based Vulnerabilities in Software-Defined Networking

Benjamin E. Ujcich (University of Illinois at Urbana-Champaign), Samuel Jero (MIT Lincoln Laboratory), Richard Skowyra (MIT Lincoln Laboratory), Steven R. Gomez (MIT Lincoln Laboratory), Adam Bates (University of Illinois at Urbana-Champaign), William H. Sanders (University of Illinois at Urbana-Champaign), Hamed Okhravi (MIT Lincoln Laboratory)

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When Match Fields Do Not Need to Match: Buffered...

Jiahao Cao (Tsinghua University; George Mason University), Renjie Xie (Tsinghua University), Kun Sun (George Mason University), Qi Li (Tsinghua University), Guofei Gu (Texas A&M University), Mingwei Xu (Tsinghua University)

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Data-Driven Debugging for Functional Side Channels

Saeid Tizpaz-Niari (University of Colorado Boulder), Pavol Černý (TU Wien), Ashutosh Trivedi (University of Colorado Boulder)

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