Publications

In Proceedings of the 42nd IEEE Symposium on Security and Privacy (S&P 2021) Our analysis lets us paint a highly detailed picture of the cookie ecosystem, discovering an intricate network of connections between players that reciprocally exchange information and include each other's content in web pages whose owners may not even be aware.

In proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020)
For federated learning systems deployed in the wild, data flaws hosted on local agents are widely witnessed. On one hand, given a large amount of training data are corrupted by systematic sensor noise and environmental perturbations, the performances of federated model training can be degraded significantly. On the other hand, it is prohibitively expensive for either clients or service providers to set up manual sanitary checks to verify the quality of data instances. In our study, we echo this challenge by proposing a collaborative and privacy-preserving machine teaching method. Specifically, we use a few trusted instances provided by teachers as benign examples in the teaching process. Our collaborative teaching approach seeks jointly the optimal tuning on the distributed training set, such that the model learned from the tuned training set predicts labels of the trusted items correctly. The proposed method couples the process of teaching and learning and thus produces directly a robust prediction model despite the extremely pervasive systematic data corruption. The experimental study on real benchmark data sets demonstrates the validity of our method.

In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020)

In Proceedings of the 41st IEEE Symposium on Security and Privacy (S&P 2020)

In Proceedings of The Web Conference (WWW 2020)
We present the first comprehensive study of the possible security and privacy implications that clicks can have from a user perspective, analyzing the disconnect that exists between what is shown to users and what actually happens after.

In Proceedings of the 2020 CHI Workshop on Designing Interactions for the Ageing Populations - Addressing Global Challenges
Older adults are disproportionately affected by scams, many of which target them specifically. We present Fraud Bingo, an intervention designed by WISE \& Healthy Aging Center in Southern California prior to 2012, that has been played by older adults throughout the United States. We also present the Scam Defender Obstacle Course (SDOC), an interactive web application that tests a user's ability to identify scams, and subsequently teaches them how to recognize the scams. SDOC is patterned after existing phishing-recognition training tools for working professionals. We present the results of running a workshop with 17 senior citizens, where we performed a controlled study that and used SDOC to measure the effectiveness of Fraud Bingo. We outline the difficulties several participants had with completing SDOC, which indicate that tools like SDOC should be tailored to the needs of older adults.

In Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020) In this paper, we study the graph-based semi-supervised learning for classifying nodes in attributed networks, where the nodes and edges possess content information. Recent approaches like graph convolution networks and attention mechanisms have been pro-posed to ensemble the first-order neighbors and incorporate the relevant neighbors. However, it is costly (especially in memory) to consider all neighbors without a prior differentiation. We propose to explore the neighborhood in a reinforcement learning setting and find a walk path well-tuned for classifying the unlabeled target nodes. We let an agent (of node classification task) walk over the graph and decide where to move to maximize classification accuracy. We define the graph walk as a partially observable Markov decision process (POMDP). The proposed method is flexible for working in both transductive and inductive setting. Extensive experiments on four data sets demonstrate that our proposed method outperforms several state-of-the-art methods. Several case studies also illustrate the meaningful movement trajectory made by the agent.