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Web tracking and advertising (WTA) nowadays are ubiquitously performed on the web, continuously compromising users' privacy. Existing defense solutions, such as widely deployed blocking tools based on filter lists and alternative machine learning based solutions proposed in prior research, have limitations in terms of accuracy and effectiveness. In this work, we propose WtaGraph, a web tracking and advertising detection framework based on Graph Neural Networks (GNNs). We first construct an attributed homogenous multi-graph (AHMG) that represents HTTP network traffic, and formulate web tracking and advertising detection as a task of GNN-based edge representation learning and classification in AHMG. We then design four components in WtaGraph so that it can (1) collect HTTP network traffic, DOM, and JavaScript data, (2) construct AHMG and extract corresponding edge and node features, (3) build a GNN model for edge representation learning and WTA detection in the transductive learning setting, and (4) use a pre-trained GNN model for WTA detection in the inductive learning setting. We evaluate WtaGraph on a dataset collected from Alexa Top 10K websites, and show that WtaGraph can effectively detect WTA requests in both transductive and inductive learning settings. Manual verification results indicate that WtaGraph can detect new WTA requests that are missed by filter lists and recognize non-WTA requests that are mistakenly labeled by filter lists. Our ablation analysis, evasion evaluation, and real-time evaluation show that WtaGraph can have a competitive performance with flexible deployment options in practice. 

Crowdsourcing is popular for large-scale data collection and labeling, but a major challenge is on detecting low-quality submissions. Recent studies have demonstrated that behavioral features of workers are highly correlated with data quality and can be useful in quality control. However, these studies primarily leveraged coarsely extracted behavioral features, and did not further explore quality control at the fine-grained level, i.e., the annotation unit level. In this paper, we investigate the feasibility and benefits of using fine-grained behavioral features, which are the behavioral features finely extracted from a worker's individual interactions with each single unit in a subtask, for quality control in crowdsourcing. We design and implement a framework named Fine-grained Behavior-based Quality Control (FBQC) that specifically extracts fine-grained behavioral features to provide three quality control mechanisms: (1) quality prediction for objective tasks, (2) suspicious behavior detection for subjective tasks, and (3) unsupervised worker categorization. Using the FBQC framework, we conduct two real-world crowdsourcing experiments and demonstrate that using fine-grained behavioral features is feasible and beneficial in all three quality control mechanisms. Our work provides clues and implications for helping job requesters or crowdsourcing platforms to further achieve better quality control. 

Abstract Web measurement is a powerful approach to studying various tracking practices that may compromise the privacy of millions of users. Researchers have built several measurement frameworks and performed a few studies to measure web tracking on the desktop environment. However, little is known about web tracking on the mobile environment, and no tool is readily available for performing a comparative measurement study on mobile and desktop environments. In this work, we built a framework called WTPatrol that allows us and other researchers to perform web tracking measurement on both mobile and desktop environments. Using WTPatrol, we performed the first comparative measurement study of web tracking on 23,310 websites that have both mobile version and desktop version web-pages. We conducted an in-depth comparison of the web tracking practices of those websites between mobile and desktop environments from two perspectives: web tracking based on JavaScript APIs and web tracking based on HTTP cookies. Overall, we found that mobile web tracking has its unique characteristics especially due to mobile-specific trackers, and it has become increasingly as prevalent as desktop web tracking. However, the potential impact of mobile web tracking is more severe than that of desktop web tracking because a user may use a mobile device frequently in different places and be continuously tracked. We further gave some suggestions to web users, developers, and researchers to defend against web tracking.