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

OcuLock: Exploring Human Visual System for Authentication in Virtual...

Shiqing Luo (Georgia State University), Anh Nguyen (Georgia State University), Chen Song (San Diego State University), Feng Lin (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

Finding Safety in Numbers with Secure Allegation Escrows

Venkat Arun (Massachusetts Institute of Technology), Aniket Kate (Purdue University), Deepak Garg (Max Planck Institute for Software Systems), Peter Druschel...

Read More

DISCO: Sidestepping RPKI's Deployment Barriers

Tomas Hlavacek (Fraunhofer SIT), Italo Cunha (Universidade Federal de Minas Gerais), Yossi Gilad (Hebrew University of Jerusalem), Amir Herzberg (University...

Read More