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Privacy in Recommender Systems

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Data Downloaded via Parachute from a NASA Super-Pressure Balloon

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In April 2023, the superBIT telescope was lifted to the Earth's stratosphere by a helium-filled super-pressure balloon to acquire astronomical imaging from above (99.5% of) the Earth's atmosphere. It was launched from New Zealand and then, for 40 days, cir ...

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Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstr ...

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Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Boi Faltings, Aleksei Triastcyn

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing ...

MDPI2022

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Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

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Many fields make use nowadays of machine learning (ML) enhanced applications for cost optimization, scheduling or forecasting, in- cluding the energy sector. However, these very ML algorithms consume a significant amount of energy, sometimes going against ...

ACM2022

Multi-Step Critiquing User Interface for Recommender Systems

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Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow users to provide feedback by critiquing them. Several algorithms for recomme ...

ASSOC COMPUTING MACHINERY2021

The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...

EPFL2020

This paper considers the problem of enhancing user privacy in common machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples form a generative adversarial network. We propose employing Bayesian ...

2020

Bayesian Differential Privacy for Machine Learning

Boi Faltings, Aleksei Triastcyn

%0 Conference Paper %T Bayesian Differential Privacy for Machine Learning %A Aleksei Triastcyn %A Boi Faltings %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E A ...

2020

MedCo(2): Privacy-Preserving Cohort Exploration and Analysis

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean Louis Raisaro, David Jules Froelicher, Mickaël Misbach

Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients i ...

IOS PRESS2020