Catuscia Palamidessi

research

∙ 05/22/2023

Analyzing the Shuffle Model through the Lens of Quantitative Information Flow

Local differential privacy (LDP) is a variant of differential privacy (D...

0 Mireya Jurado, et al. ∙

research

∙ 04/25/2023

(Local) Differential Privacy has NO Disparate Impact on Fairness

In recent years, Local Differential Privacy (LDP), a robust privacy-pres...

10 Héber H. Arcolezi, et al. ∙

research

∙ 10/01/2022

Frequency Estimation of Evolving Data Under Local Differential Privacy

Collecting and analyzing evolving longitudinal data has become a common ...

0 Héber H. Arcolezi, et al. ∙

research

∙ 09/16/2022

Survey on Fairness Notions and Related Tensions

Automated decision systems are increasingly used to take consequential d...

0 Guilherme Alves, et al. ∙

research

∙ 09/09/2022

Minimizing Information Leakage under Padding Constraints

An attacker can gain information of a user by analyzing its network traf...

0 Sebastian Simon, et al. ∙

research

∙ 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...

0 Héber H. Arcolezi, et al. ∙

research

∙ 08/30/2022

On the (Im)Possibility of Estimating Various Notions of Differential Privacy

We analyze to what extent final users can infer information about the le...

0 Daniele Gorla, et al. ∙

research

∙ 08/24/2022

Reconstruction of the distribution of sensitive data under free-will privacy

The local privacy mechanisms, such as k-RR, RAPPOR, and the geo-indistin...

0 Ehab ElSalamouny, et al. ∙

research

∙ 06/21/2022

Three-way optimization of privacy and utility of location data

With the recent bloom of data and the drive towards an information-based...

0 Sayan Biswas, et al. ∙

research

∙ 06/14/2022

Causal Discovery for Fairness

It is crucial to consider the social and ethical consequences of AI and ...

0 Rūta Binkytė-Sadauskienė, et al. ∙

research

∙ 06/07/2022

Group privacy for personalized federated learning

Federated learning is a type of collaborative machine learning, where pa...

0 Filippo Galli, et al. ∙

research

∙ 06/04/2022

A privacy preserving querying mechanism with high utility for electric vehicles

With the recent rise in awareness about advancing towards a sustainable ...

0 Ugur Ilker Atmaca, et al. ∙

research

∙ 05/18/2022

Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response

Most differentially private (DP) algorithms assume a central model in wh...

0 Sayan Biswas, et al. ∙

research

∙ 05/09/2022

Tight Differential Privacy Blanket for Shuffle Model

With the recent bloom of focus on digital economy, the importance of per...

0 Sayan Biswas, et al. ∙

research

∙ 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...

1 Héber H. Arcolezi, et al. ∙

research

∙ 05/03/2022

Universal Optimality and Robust Utility Bounds for Metric Differential Privacy

We study the privacy-utility trade-off in the context of metric differen...

0 Natasha Fernandes, et al. ∙

research

∙ 03/17/2022

Leveraging Adversarial Examples to Quantify Membership Information Leakage

The use of personal data for training machine learning systems comes wit...

0 Ganesh Del Grosso, et al. ∙

research

∙ 03/11/2022

Identifiability of Causal-based Fairness Notions: A State of the Art

Machine learning algorithms can produce biased outcome/prediction, typic...

0 Karima Makhlouf, et al. ∙

research

∙ 11/30/2021

Establishing the Price of Privacy in Federated Data Trading

Personal data is becoming one of the most essential resources in today's...

0 Kangsoo Jung, et al. ∙

research

∙ 07/14/2021

On the impossibility of non-trivial accuracy under fairness constraints

One of the main concerns about fairness in machine learning (ML) is that...

0 Carlos Pinzón, et al. ∙

research

∙ 06/27/2021

An Incentive Mechanism for Trading Personal Data in Data Markets

With the proliferation of the digital data economy, digital data is cons...

0 Sayan Biswas, et al. ∙

research

∙ 06/04/2021

DOCTOR: A Simple Method for Detecting Misclassification Errors

Deep neural networks (DNNs) have shown to perform very well on large sca...

0 Federica Granese, et al. ∙

research

∙ 05/09/2021

Bounding Information Leakage in Machine Learning

Machine Learning services are being deployed in a large range of applica...

0 Ganesh Del Grosso, et al. ∙

research

∙ 12/22/2020

Information Leakage Games: Exploring Information as a Utility Function

A common goal in the areas of secure information flow and privacy is to ...

0 Mário S. Alvim, et al. ∙

research

∙ 11/06/2020

The Bayes Security Measure

Security system designers favor worst-case security measures, such as th...

0 Konstantinos Chatzikokolakis, et al. ∙

research

∙ 10/19/2020

Survey on Causal-based Machine Learning Fairness Notions

Addressing the problem of fairness is crucial to safely use machine lear...

0 Karima Makhlouf, et al. ∙

research

∙ 06/30/2020

On the Applicability of ML Fairness Notions

ML-based predictive systems are increasingly used to support decisions w...

0 Karima Makhlouf, et al. ∙

research

∙ 05/09/2020

Estimating g-Leakage via Machine Learning

This paper considers the problem of estimating the information leakage o...

0 Marco Romanelli, et al. ∙

research

∙ 01/27/2020

Feature selection in machine learning: Rényi min-entropy vs Shannon entropy

Feature selection, in the context of machine learning, is the process of...

0 Catuscia Palamidessi, et al. ∙

research

∙ 09/06/2019

Full Convergence of the Iterative Bayesian Update and Applications to Mechanisms for Privacy Protection

The iterative Bayesian update (IBU) and the matrix inversion (INV) are t...

0 Ehab ElSalamouny, et al. ∙

research

∙ 06/28/2019

Utility-Preserving Privacy Mechanisms for Counting Queries

Differential privacy (DP) and local differential privacy (LPD) are frame...

0 Natasha Fernandes, et al. ∙

research

∙ 04/01/2019

Generating Optimal Privacy-Protection Mechanisms via Machine Learning

We consider the problem of obfuscating sensitive information while prese...

0 Marco Romanelli, et al. ∙

research

∙ 02/04/2019

F-BLEAU: Fast Black-box Leakage Estimation

We consider the problem of measuring how much a system reveals about its...

0 Giovanni Cherubin, et al. ∙

research

∙ 05/03/2018

Metric-based local differential privacy for statistical applications

Local differential privacy (LPD) is a distributed variant of differentia...

0 Mário S. Alvim, et al. ∙

research

∙ 03/27/2018

A Game-Theoretic Approach to Information-Flow Control via Protocol Composition

In the inference attacks studied in Quantitative Information Flow (QIF),...

0 Mário S. Alvim, et al. ∙

research

∙ 02/27/2018

Leakage and Protocol Composition in a Game-Theoretic Perspective

In the inference attacks studied in Quantitative Information Flow (QIF),...

0 Mário S. Alvim, et al. ∙

Catuscia Palamidessi

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