Today image hosting platforms are a popular way to store and share images with family members and friends. However, such platforms typically have full access to images raising privacy concerns. In this talk we motivate the need for image privacy protection techniques that preserve certain visual features in images while hiding other information, to balance privacy and usability in the context of cloud-based image storage services. We will explore privacy schemes that use cryptographic, adversarial perturbation and semantic perturbation approaches and discuss their pros and cons.
Our vision is a world where limitless connectivity improves lives, redefines business, and pioneers a sustainable future. Since the completion of the first 5G specifications in 2018, there has been a substantial expansion of commercial 5G deployments happening throughout the world, with networks providing new communication capabilities and services set to transform society. Undoubtedly, the ongoing transformation will eventually give rise to challenges beyond what 5G can meet.
Modern information processing systems increasingly demand the ability to continuously process incoming streaming data in a timely and reliable manner. Data streams arise in diverse applications ranging from patient monitoring in healthcare to real-time decision-making in emerging Internet of Things (IoT) systems. In this talk, I will present research on the design of programming abstractions for stream processing that enable guarantees of correctness and predictable performance.
Computer and Information Science Department Head Reza Rejaie will meet with grad students.
Prof. Childs will arrange this event with the UO CIS grad students.
We will tell the story of how Moebius functions may be used to count by inclusion-exclusion topologically. In particular, we will discuss combinatorial-topological tools that have been remarkably effective at doing this, namely lexicographic shellability and more recently also discrete Morse theory. Some of the most compelling applications have been to theoretical computer science, a topic we will highlight along the way. We will not assume familiarity with topology or with the more algebraic and topological sides of discrete mathematics arising in this talk.
Federated learning (FL) is an emerging technique for model training from decentralized data. Compared to learning from data in a central storage, FL has benefits of privacy preservation and communication bandwidth reduction. A challenge in FL is that data and model characteristics can vary largely across different tasks, and an FL task with improper configuration could waste a lot of computation/communication resources and may cause the trained model to diverge from the optimal result.
Cybersecurity has been long thought of as a technical challenge for computer science and engineering departments to address. This talk will highlight cybersecurity as a "wicked problem" that needs a complex solution comprised of expertise in computer science, law, psychology, business, and other disciplines to effectively address this problem.
To make sense of network traffic telemetry (NetFlow, IPFIX, sFlow, VPC Flow Logs, eBPF, etc) in modern, orchestrated and diverse networks, it's necessary to enrich those telemetry streams with context. Kentik's ingest layer does this live with millions of flows/second, and started doing so with BGP routing, but also has other large-scale and generalized mechanisms for doing high-speed lookup and enrichment/decoration/coloring of incoming data, each of which can have tens of millions of dynamically changing association rules.