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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Bobtail: Improved Blockchain Security with Low-Variance Mining

George Bissias (University of Massachusetts Amherst), Brian N. Levine (University of Massachusetts Amherst)

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FUSE: Finding File Upload Bugs via Penetration Testing

Taekjin Lee (KAIST, ETRI), Seongil Wi (KAIST), Suyoung Lee (KAIST), Sooel Son (KAIST)

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Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer

Zhongjie Ba (Zhejiang University and McGill University), Tianhang Zheng (University of Toronto), Xinyu Zhang (Zhejiang University), Zhan Qin (Zhejiang University), Baochun Li (University of Toronto), Xue Liu (McGill University), Kui Ren (Zhejiang University)

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Complex Security Policy? A Longitudinal Analysis of Deployed Content...

Sebastian Roth (CISPA Helmholtz Center for Information Security), Timothy Barron (Stony Brook University), Stefano Calzavara (Università Ca' Foscari Venezia), Nick Nikiforakis (Stony Brook University), Ben Stock (CISPA Helmholtz Center for Information Security)

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