Privacy-Enhancing Technologies for Mobile Applications and Services

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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 ...

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 ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

Interactive mobile applications like web browsing and gaming are known to benefit significantly from low latency networking, as applications communicate with cloud servers and other users' devices. Emerging mobile channel standards have not met these needs ...

Security and privacy-sensitive smartphone applications use trusted execution environments (TEEs) to protect sensitive operations from malicious code. By design, TEEs have privileged access to the entire system but expose little to no insight into their inn ...

The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body part ...

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 ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

Privacy-Enhancing Technologies for Mobile Applications and Services

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Multi-agent Learning with Privacy Guarantees

Sustainability and Ethicality are Peripheral to Students’ Software Design

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

DChannel: Accelerating Mobile ApplicationsWith Parallel High-bandwidth and Low-latency Channels

TEEzz: Fuzzing Trusted Applications on COTS Android Devices

Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Privacy-preserving federated neural network training and inference