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In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywo ...

The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains unta ...

The conceptual design phase is a fascinating moment to observe how a design task is interpreted, as the (often implicit) relative importance students accord to the various requirements and constraints offers a window into the thinking underpinning their de ...

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

Keeping track of Internet latency is a classic measurement problem. Open measurement platforms like RIPE Atlas are a great solution, but they also face challenges: preventing network overload that may result from uncontrolled active measurements, and maint ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

Water diversions from rivers and torrents for anthropic uses of the resource alter the natural flow regime. As a measure, environmental flows have been prescribed and often are enforced by law to follow policies (e.g., minimal flow, proportional redistribu ...

Several optimization scenarios involve multiple agents that desire to protect the privacy of their preferences. There are distributed algorithms for constraint optimization that provide improved privacy protection through secure multiparty computation. How ...

Smartphones present many interesting opportunities for survey research, particularly through the use of mobile data collection applications (apps). There is still much to learn, however, about how to integrate apps in general population surveys. Recent stu ...

Privacy-aware and highly-available OSN profiles

Chat with Graph Search

Global, Passive Detection of Connection Tampering

Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models

Sustainability and Ethicality are Peripheral to Students’ Software Design

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Using Gaming Footage as a Source of Internet Latency Information

Multi-agent Learning with Privacy Guarantees

Dynamic environmental flows using hydrodynamic-based solutions for sustainable hydropower

Differentially Private Multi-Agent Constraint Optimization

Data Privacy Concerns as a Source of Resistance to Complete Mobile Data Collection Tasks Via a Smartphone App