Security, privacy, and abuse prevention

Automated static dataflow analysis is an effective technique for detecting security critical issues like sensitive data leak, and vulnerability to injection attacks. Ensuring high precision and recall requires an analysis that is context, field and object sensitive. However, it is challenging to attain high precision and recall and scale to large industrial code bases. Compositional style analyses in which

Many public pre-trained word embeddings have been shown to encode different types of biases. Embeddings are often obtained from training on large pre-existing corpora, and therefore resulting biases can be a reflection of unfair representations in the original data. Bias, in this scenario, is a challenging problem since current mitigation techniques require knowing and understanding existing biases in the

Over the past few years, Federated Learning (FL) has become an emerging machine learning technique to tackle data privacy challenges through collaborative training. In the Federated Learning algorithm, the clients submit a locally trained model, and the server aggregates these parameters until convergence. Despite significant efforts that have been made to FL in fields like computer vision, audio, and natural

Irrespective of the intent, malicious or benign, behind the origin of non-human traffic on sponsored advertising pages, failure to detect such unwanted traffic results in deterioration of advertiser performance metrics. Invalid (i.e., robotic) ad traffic is frequently driven by IP addresses (or address ranges) that are exclusively dedicated to VPNs, hosting or proxy services, Tor networks, as well as by

Rapid growth of deep learning models in recent years for robot and fraud detection has led to significant improvement in precision and recall but has also created a challenge for explainability and trust in the model decisions. In this paper, we propose a scalable multitiered framework that generates explainable network request level signatures for crawler bots on a large e-commerce advertising program.