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09/19/2023
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning
Graphs are a widely used data structure for collecting and analyzing rel...
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09/04/2023
Revealing the True Cost of Local Privacy: An Auditing Perspective
This paper introduces the LDP-Auditor framework for empirically estimati...
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08/29/2023
Probabilistic Dataset Reconstruction from Interpretable Models
Interpretability is often pointed out as a key requirement for trustwort...
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02/28/2023
Membership Inference Attack for Beluga Whales Discrimination
To efficiently monitor the growth and evolution of a particular wildlife...
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10/01/2022
Frequency Estimation of Evolving Data Under Local Differential Privacy
Collecting and analyzing evolving longitudinal data has become a common ...
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09/04/2022
On the Risks of Collecting Multidimensional Data Under Local Differential Privacy
The private collection of multiple statistics from a population is a fun...
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09/02/2022
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction
In recent years, a growing body of work has emerged on how to learn mach...
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09/01/2022
Fair mapping
To mitigate the effects of undesired biases in models, several approache...
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05/05/2022
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python
This paper introduces the Python package for multiple frequency estimat...
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06/14/2021
Characterizing the risk of fairwashing
Fairwashing refers to the risk that an unfair black-box model can be exp...
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09/03/2020
Model extraction from counterfactual explanations
Post-hoc explanation techniques refer to a posteriori methods that can b...
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07/03/2020
Online publication of court records: circumventing the privacy-transparency trade-off
The open data movement is leading to the massive publishing of court rec...
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03/23/2020
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks
With the widespread adoption of the quantified self movement, an increas...
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09/26/2019
GAMIN: An Adversarial Approach to Black-Box Model Inversion
Recent works have demonstrated that machine learning models are vulnerab...
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09/09/2019
Learning Fair Rule Lists
The widespread use of machine learning models, especially within the con...
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06/19/2019
Agnostic data debiasing through a local sanitizer learnt from an adversarial network approach
The widespread use of automated decision processes in many areas of our ...
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01/28/2019
Fairwashing: the risk of rationalization
Black-box explanation is the problem of explaining how a machine learnin...
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05/24/2018