Kevin Alejandro Roundy

In Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI 2019)
To identify needs for improvement in security products, we study security concerns raised in Norton Security customer support chats. We found that many consumers face technical support scams and are susceptible to them. Findings also show the value of customer support centers in that 96% of customers that reach out for support in relation to scams have not paid the scammers

In Proceedings of ACM CHI Conference on Human Factors in Computing Systems (CHI 2020) (Honorable Mention Award)
Our online survey of 902 individuals studies the reasons for which users struggle to adhere to expert-recommended security, privacy, and identity-protection practices. We examined 30 of these practices, finding that gender, education, technical background, and prior negative experiences correlate with practice adoption levels. We found that practices were abandoned when they were perceived as low-value, inconvenient, or when overridden by subjective judgment. We discuss how tools and expert recommendations can better align to user needs.

In Proceedings of the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017)
Mapping binary files into software packages enables malware detection and other tasks, but is challenging. By combining installation data with file metadata that we summarize into sketches, from millions of machines and billions of files, we can use efficient approximate clustering techniques to map files to applications automatically and reliably.

In Proceedings of the 2019 ENISA Annual Privacy Forum (APF 2019)
We specifically analyze how the “right-to-be-forgotten” provided by the European Union General Data Protection Regulation can be implemented on current machine learning models and which techniques can be used to build future models that can forget. This document also serves as a call-to-action for researchers and policy-makers to identify other technologies that can be used for this purpose.

In Proceedings of the Interactive Data Exploration and Analytics Workshop (IDEA 2018)
We enable security incident responders to dispatch multiple similar security incidents at once through an intuitive user interface. The heart of our algorithm is a visualized hierarchical clustering technique that enables responders to identify the appropriate level of cluster granularity at which to dispatch multiple incidents.

In Proceedings of the 25th International World Wide Web Conference (WWW), 2016
We study the problem of determining the proper aggregation granularity for a stream of time-stamped edges. To this end, we propose ADAGE and demonstrate its value in automatically finding the appropriate aggregation intervals on edge streams for belief propagation to detect malicious files and machines.

IEEE Transactions on Visualization and Computer Graphics (TVCG), 24(1), 2018, Presented at the 2017 IEEE Conference on Visual Analytics Science and Technology (VAST), 2017
We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of graph query results. VIGOR contributes an exemplar-based interaction technique and a feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world cybersecurity problems.

In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019)
In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. The focus is to find strategies to organize distributed agents to jointly select a compact subset of data that can be used to train a global model. The global model should achieve nearly the same performance as if the central learner had access to all the data, but the central learner only has access to the selected subset, and each agent only has access to their own data. The goal of this research is to find good strategies to train global models while giving some control back to agents.