Xiaokuan Zhang (The Ohio State University), Jihun Hamm (The Ohio State University), Michael K. Reiter (University of North Carolina at Chapel Hill), Yinqian Zhang (The Ohio State University)

Machine learning empowers traffic-analysis attacks that breach users' privacy from their encrypted traffic. Recent advances in deep learning drastically escalate such threats.
One prominent example demonstrated recently is a traffic-analysis attack against video streaming by using convolutional neural networks. In this paper, we explore the adaption of techniques previously used in the domains of adversarial machine learning and differential privacy to mitigate the machine-learning-powered analysis of streaming traffic.

Our findings are twofold. First, constructing adversarial samples effectively confounds an adversary with a predetermined classifier but is less effective when the adversary can adapt to the defense by using alternative classifiers or training the classifier with adversarial samples. Second, differential-privacy guarantees are very effective against such statistical-inference-based traffic analysis, while remaining agnostic to the machine learning classifiers used by the adversary. We propose two mechanisms for enforcing differential privacy for encrypted streaming traffic, and evaluate their security and utility. Our empirical implementation and evaluation suggest that the proposed statistical privacy approaches are promising solutions in the underlying scenarios.

View More Papers

Robust Performance Metrics for Authentication Systems

Shridatt Sugrim (Rutgers University), Can Liu (Rutgers University), Meghan McLean (Rutgers University), Janne Lindqvist (Rutgers University)

Read More

Profit: Detecting and Quantifying Side Channels in Networked Applications

Nicolás Rosner (University of California, Santa Barbara), Ismet Burak Kadron (University of California, Santa Barbara), Lucas Bang (Harvey Mudd College), Tevfik Bultan (University of California, Santa Barbara)

Read More

Don't Trust The Locals: Investigating the Prevalence of Persistent...

Marius Steffens (CISPA Helmholtz Center for Information Security), Christian Rossow (CISPA Helmholtz Center for Information Security), Martin Johns (TU Braunschweig), Ben Stock (CISPA Helmholtz Center for Information Security)

Read More

Anonymous Multi-Hop Locks for Blockchain Scalability and Interoperability

Giulio Malavolta (Friedrich-Alexander University Erlangen-Nürnberg), Pedro Moreno Sanchez (TU Wien), Clara Schneidewind (TU Wien), Aniket Kate (Purdue University), Matteo Maffei (TU Wien)

Read More