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

PhantomCache: Obfuscating Cache Conflicts with Localized Randomization

Qinhan Tan (Zhejiang University), Zhihua Zeng (Zhejiang University), Kai Bu (Zhejiang University), Kui Ren (Zhejiang University)

Read More

Let's Revoke: Scalable Global Certificate Revocation

Trevor Smith (Brigham Young University), Luke Dickenson (Brigham Young University), Kent Seamons (Brigham Young University)

Read More

Metal: A Metadata-Hiding File-Sharing System

Weikeng Chen (UC Berkeley), Raluca Ada Popa (UC Berkeley)

Read More

Precisely Characterizing Security Impact in a Flood of Patches...

Qiushi Wu (University of Minnesota), Yang He (University of Minnesota), Stephen McCamant (University of Minnesota), Kangjie Lu (University of Minnesota)

Read More