Meenatchi Sundaram Muthu Selva Annamalai (University College London), Igor Bilogrevic (Google), Emiliano De Cristofaro (University of California, Riverside)

Browser fingerprinting often provides an attractive alternative to third-party cookies for tracking users across the web. In fact, the increasing restrictions on third-party cookies placed by common web browsers and recent regulations like the GDPR may accelerate the transition. To counter browser fingerprinting, previous work proposed a number of techniques to detect its prevalence and severity. However, most – if not all – of those techniques rely on 1) centralized web crawls and/or 2) computationally-intensive operations to extract and process signals (e.g., information-flow and static analysis).

To address these limitations, we present FP-Fed, the first distributed system for browser fingerprinting detection. Using FP-Fed, users collaboratively train on-device models based on their real browsing patterns, without sharing their training data with a central entity, by relying on Differentially Private Federated Learning (DP-FL). To demonstrate its feasibility and effectiveness, we evaluate FP-Fed’s performance on a set of 20k popular websites with different privacy levels, numbers of participants, and features extracted from the scripts. Our experiments show that FP-Fed achieves reasonably high detection performance and can perform both training and inference efficiently, on-device, by only relying on runtime signals extracted from the execution trace, without requiring any resource-intensive operation.

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MOCK: Optimizing Kernel Fuzzing Mutation with Context-aware Dependency

Jiacheng Xu (Zhejiang University), Xuhong Zhang (Zhejiang University), Shouling Ji (Zhejiang University), Yuan Tian (UCLA), Binbin Zhao (Georgia Institute of Technology), Qinying Wang (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University)

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WIP: Modeling and Detecting Falsified Vehicle Trajectories Under Data...

Jun Ying, Yiheng Feng (Purdue University), Qi Alfred Chen (University of California, Irvine), Z. Morley Mao (University of Michigan and Google)

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Predictive Context-sensitive Fuzzing

Pietro Borrello (Sapienza University of Rome), Andrea Fioraldi (EURECOM), Daniele Cono D'Elia (Sapienza University of Rome), Davide Balzarotti (Eurecom), Leonardo Querzoni (Sapienza University of Rome), Cristiano Giuffrida (Vrije Universiteit Amsterdam)

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