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.

View More Papers

IMP4GT: IMPersonation Attacks in 4G NeTworks

David Rupprecht (Ruhr University Bochum), Katharina Kohls (Ruhr University Bochum), Thorsten Holz (Ruhr University Bochum), Christina Poepper (NYU Abu Dhabi)

Read More

Snappy: Fast On-chain Payments with Practical Collaterals

Vasilios Mavroudis (University College London), Karl Wüst (ETH Zurich), Aritra Dhar (ETH Zurich), Kari Kostiainen (ETH Zurich), Srdjan Capkun (ETH Zurich)

Read More

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)

Read More

µRAI: Securing Embedded Systems with Return Address Integrity

Naif Saleh Almakhdhub (Purdue University and King Saud University), Abraham A. Clements (Sandia National Laboratories), Saurabh Bagchi (Purdue University), Mathias Payer (EPFL)

Read More