PriBots: Conversational Privacy with Chatbots

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The recent rise in both popularity and performance of large language models has garnered considerable interest regarding their applicability to education. Technologies like ChatGPT, which can engage in human-like dialog, have already disrupted educational ...

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning-a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and ...

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 ...

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while runnin ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

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 ...

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 ...

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 ...

PriBots: Conversational Privacy with Chatbots

Chat with Graph Search

Prompting Large Language Models to Power Educational Chatbots

On the (In)security of Peer-to-Peer Decentralized Machine Learning

Sustainability and Ethicality are Peripheral to Students’ Software Design

A Multimodal Dataset for Automatic Edge-AI Cough Detection

Discovering Influencers in Opinion Formation Over Social Graphs

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

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

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Medical data sharing and privacy: a false dichotomy?