Privacy and Utility Allied

Universal Optimality and Robust Utility Bounds for Metric Differential Privacy

Autori: Natasha Fernandes; Annabelle McIver; Catuscia Palamidessi; Ming Ding
Pubblicato in: IEEE 35th Computer Security Foundations Symposium (CSF), 2022, ISBN 978-1-6654-8417-6
Editore: IEEE
DOI: 10.1109/csf54842.2022.9919647

Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling

Autori: Andreas Athanasiou, Kangsoo Jung, Catuscia Palamidessi
Pubblicato in: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2025, Pagina/e 5036-5038
Editore: ACM
DOI: 10.1145/3658644.3691411

On the Impossibility of non-Trivial Accuracy in Presence of Fairness Constraints

Autori: Carlos Pinzón; Catuscia Palamidessi; Pablo Piantanida; Frank Valencia
Pubblicato in: 36th AAAI Conference on Artificial Intelligence, 2022
Editore: Association for the Advancement of Artificial Intelligence
DOI: 10.1609/aaai.v36i7.20770

Causal Discovery for Fairness

Autori: Binkytė-Sadauskienė, Rūta; Makhlouf, Karima; Pinzón, Carlos; Zhioua, Sami; Palamidessi, Catuscia
Pubblicato in: Proceedings of Machine Learning Research, 2023
Editore: Proceedings of Machine Learning Research

Identifiability of Causal-based ML Fairness Notions

Autori: Makhlouf, Karima; Zhioua, Sami; Palamidessi, Catuscia
Pubblicato in: IEEE International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2022, ISSN 2472-7555
Editore: IEEE
DOI: 10.1109/cicn56167.2022.10008263

Analyzing the Shuffle Model Through the Lens of Quantitative Information Flow

Autori: Jurado, Mireya; Gonze, Ramon, Goncalves; Alvim, Mário, S; Palamidessi, Catuscia
Pubblicato in: Proceedings of the IEEE 36th Computer Security Foundations Symposium (CSF), 2023
Editore: IEEE
DOI: 10.1109/csf57540.2023.00033

Membership Inference Attacks via Adversarial Examples

Autori: Jalalzai, Hamid; Kadoche, Elie; Leluc, Rémi; Plassier, Vincent
Pubblicato in: Trustworthy and Socially Responsible Machine Learning (NeurIPS workshop), 2022
Editore: OpenReview.net

Generalized Iterative Bayesian Update and Applications to Mechanisms for Privacy Protection

Autori: Ehab ElSalamouny, Catuscia Palamidessi
Pubblicato in: 2020 IEEE European Symposium on Security and Privacy (EuroS&P), 2020, Pagina/e 490-507, ISBN 978-1-7281-5087-1
Editore: IEEE
DOI: 10.1109/eurosp48549.2020.00038

A Formal Information-Theoretic Leakage Analysis of Order-Revealing Encryption

Autori: Mireya Jurado; Catuscia Palamidessi; Geoffrey Smith
Pubblicato in: IEEE 34th Computer Security Foundations Symposium (CSF), 2021, ISBN 978-1-7281-7607-9
Editore: IEEE
DOI: 10.1109/csf51468.2021.00046

Modern Applications of Game-Theoretic Principles

Autori: Palamidessi, Catuscia; Romanelli, Marco
Pubblicato in: CONCUR 2020 - 31st International Conference on Concurrency Theory, Sep 2020, Vienne / Virtual, Austria., Numero 171, 2020, Pagina/e 4:1--4:9
Editore: Schloss Dagstuhl - Leibniz-Zentrum fur Informatik
DOI: 10.4230/lipics.concur.2020.4

On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms

Autori: Héber H. Arcolezi; Selene Cerna; Catuscia Palamidessi
Pubblicato in: "DBSec 2023 - 37th IFIP Annual Conference on Data and Applications Security and Privacy, Vijay Atluri; Anna Lisa Ferrara, Jul 2023, Sophia Antipolis, France. pp.165-183, ⟨10.1007/978-3-031-37586-6_11⟩", 2023, ISBN 978-3-031-37585-9
Editore: Springer-Verlag
DOI: 10.1007/978-3-031-37586-6_11

On the duality of privacy and fairness

Autori: Alvim, Mário, S.; Fernandes, Natasha; Nogueira, Bruno, D; Palamidessi, Catuscia; Silva, Thiago, V A
Pubblicato in: International Conference on AI and the Digital Economy (CADE 2023),, 2023, ISBN 978-1-83953-959-6
Editore: IET
DOI: 10.1049/icp.2023.2563

DOCTOR: A Simple Method for Detecting Misclassification Errors

Autori: Granese, Federica; Romanelli, Marco; Gorla, Daniele; Palamidessi, Catuscia; Piantanida, Pablo
Pubblicato in: Advances in Neural Information Processing Systems (NeurIPS), 2021, Virtual event, United States, Numero 34, 2021, ISSN 1049-5258
Editore: Curran Associates Inc. (Printed version) and Neural Information Processing Systems (Online version)
DOI: 10.48550/arxiv.2106.02395

Enhanced Models for Privacy and Utility in Continuous-Time Diffusion Networks

Autori: Daniele Gorla, Federica Granese, Catuscia Palamidessi
Pubblicato in: Theoretical Aspects of Computing – ICTAC 2019 - 16th International Colloquium, Hammamet, Tunisia, October 31 – November 4, 2019, Proceedings, Numero 11884, 2019, Pagina/e 313-331, ISBN 978-3-030-32504-6
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-32505-3_18

Bayes Security: A Not So Average Metric

Autori: Chatzikokolakis, Konstantinos; Cherubin, Giovanni; Palamidessi, Catuscia; Troncoso, Carmela
Pubblicato in: Proceedings of the IEEE 36th Computer Security Foundations Symposium (CSF), 2023
Editore: IEEE
DOI: 10.1109/csf57540.2023.00011

Estimating g-Leakage via Machine Learning

Autori: Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi, Pablo Piantanida
Pubblicato in: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 2020, Pagina/e 697-716, ISBN 9781450370899
Editore: ACM
DOI: 10.1145/3372297.3423363

Local Methods for Privacy Protection and Impact on Fairness

Autori: Catuscia Palamidessi
Pubblicato in: CODASPY '23: Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy, 2023, ISBN 9798400700675
Editore: ACM
DOI: 10.1145/3577923.3587263

On the Application and Impact of differential privacy and Fairness in Ambulance Engagement Time Prediction

Autori: Cerna, Selene; Palamidessi, Catuscia
Pubblicato in: ICLR 2023 - The First Tiny Papers Track at ICLR 2023, 2023
Editore: OpenReview.net

MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors

Autori: Granese, Federica; Picot, Marine; Romanelli, Marco; Messina, Francisco; Piantanida, Pablo
Pubblicato in: "ECML PKDD 2022 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2022, Grenoble, France. ⟨10.1007/978-3-031-26409-2_18⟩", 2022, ISBN 978-3-031-26408-5
Editore: Springer
DOI: 10.1007/978-3-031-26409-2_18

Optimal Obfuscation Mechanisms via Machine Learning

Autori: Marco Romanelli, Kostantinos Chatzikokolakis, Catuscia Palamidessi
Pubblicato in: 2020 IEEE 33rd Computer Security Foundations Symposium (CSF), 2020, Pagina/e 153-168, ISBN 978-1-7281-6572-1
Editore: IEEE
DOI: 10.1109/csf49147.2020.00019

Group Privacy for Personalized Federated Learning

Autori: Galli, Filippo; Biswas, Sayan; Jung, Kangsoo; Cucinotta, Tommaso; Palamidessi, Catuscia
Pubblicato in: Proceedings of the 9th International Conference on Information Systems Security and Privacy - ICISSP, 2023, 2023
Editore: SciTePress
DOI: 10.5220/0011885000003405

Tight differential privacy blanket for shuff model

Autori: Biswas, Sayan; Jung, Kangsoo; Palamidessi, Catuscia
Pubblicato in: CADE 2022 - Competitive Advantage in the Digital Economy, 2022, ISBN 978-1-83953-742-4
Editore: IEEE
DOI: 10.1049/icp.2022.2041

An Incentive Mechanism for Trading Personal Data in Data Markets

Autori: Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi
Pubblicato in: Theoretical Aspects of Computing – ICTAC, 2021, ISBN 978-3-030-85314-3
Editore: Springer
DOI: 10.1007/978-3-030-85315-0_12

Obfuscation Padding Schemes that Minimize Rényi Min-Entropy for Privacy

Autori: Simon, Sebastian; Petrui, Cezara; Pinzón, Carlos; Palamidessi, Catuscia
Pubblicato in: "International Conference on Information Security Practice and Experience, Aug 2023, Coppenhagen, Denmark. pp.74-90, ⟨10.1007/978-981-99-7032-2_5⟩", 2023, ISBN 978-981-99-7031-5
Editore: Springer
DOI: 10.1007/978-981-99-7032-2_5

Causal Discovery Under Local Privacy

Autori: Ruta Binkyte, Carlos Antonio Pinzón, Szilvia Lestyán, Kangsoo Jung, Héber Hwang Arcolezi, Catuscia Palamidessi
Pubblicato in: Causal Learning and Reasoning, Numero 236, 2024, ISSN 2640-3498
Editore: PMLR

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

Autori: Gorla, Daniele; Jalouzot, Louis; Granese, Federica; Palamidessi, Catuscia; Piantanida, Pablo
Pubblicato in: Proceedings of the 24th Italian Conference on Theoretical Computer Science, Numero 3587, 2023, Pagina/e 219--224
Editore: CEUR-WS.org

Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python

Autori: Héber H. Arcolezi; Jean-François Couchot; Sébastien Gambs; Catuscia Palamidessi; Majid Zolfaghari
Pubblicato in: Computer Security - ESORICS 2022: 27th European Symposium on Research in Computer Security, 2022, ISBN 9783031171420
Editore: Springer
DOI: 10.1007/978-3-031-17143-7_40

A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results

Autori: Karima Makhlouf, Tamara Stefanović, Héber H. Arcolezi, Catuscia Palamidessi
Pubblicato in: 2024 IEEE 37th Computer Security Foundations Symposium (CSF), 2024, Pagina/e 1-16, ISBN 979-8-3503-6203-9
Editore: IEEE
DOI: 10.1109/csf61375.2024.00039

Impact of sampling on locally differentially private data collection

Autori: Biswas, Sayan; Cormode, Graham; Maple, Carsten
Pubblicato in: Proceedings of the Eight Conference on Competitive Advantage in the Digital Economy (CADE), 2022
Editore: IET
DOI: 10.1049/icp.2022.2042

(Local) Differential Privacy has NO Disparate Impact on Fairness

Autori: Héber H. Arcolezi; Karima Makhlouf; Catuscia Palamidessi
Pubblicato in: Data and Applications Security and Privacy (DBSec 2023), 2023, ISBN 978-3-031-37585-9
Editore: Springer
DOI: 10.1007/978-3-031-37586-6_1

Frequency Estimation of Evolving Data Under Local Differential Privacy

Autori: Arcolezi, Héber Hwang; Palamidessi, Catuscia; Pinzón, Carlos; Gambs, Sébastien
Pubblicato in: EDBT 2023 - 26th International Conference on Extending Database Technology, 2023
Editore: OpenProceedings.org
DOI: 10.48786/edbt.2023.44

Leveraging Adversarial Examples to Quantify Membership Information Leakage

Autori: Ganesh Del Grosso; Hamid Jalalzai; Georg Pichler; Catuscia Palamidessi; Pablo Piantanida
Pubblicato in: Conference on Computer Vision and Pattern Recognition (CVPR), 2022, ISBN 978-1-6654-6947-0
Editore: IEEE
DOI: 10.1109/cvpr52688.2022.01015

PRIVIC: A privacy-preserving method for incremental collection of location data

Autori: Biswas, Sayan; Palamidessi, Catuscia
Pubblicato in: Proceedings on Privacy Enhancing Technologies, 2024, ISBN 978-3-031-26408-5
Editore: De Gruyter
DOI: 10.56553/popets-2024-0033

Understanding and optimizing the trade-off between privacy and utility from a foundational perspective

Autori: Biswas, Sayan
Pubblicato in: https://hal.science/tel-04407120, Numero 11, 2023
Editore: Institute Polytechnique de Paris

Towards Securing Machine Learning Algorithms

Autori: Granese, Federica
Pubblicato in: 2023, ISBN 978-3-031-26408-5
Editore: Institute Polytechnique de Paris

Exploring fairness and privacy in machine learning

Autori: Pinzón, Carlos
Pubblicato in: https://hal.science/tel-04407152, 2023
Editore: Institut Polytechnique de Paris

Leakage of Sensitive Data from Deep Neural Networks

Autori: Del Grosso, Ganesh
Pubblicato in: https://hal.science/tel-04407131, 2023
Editore: Institte Polytechnqiue de Paris

Advancing Ethical AI: Methods for fairness enhancement leveraging on causality and under privacy constraints

Autori: Ruta Binkyte
Pubblicato in: 2023
Editore: Institute Polytechnique de Paris

Securing Machine Learning Algorithms

Autori: Granese, Federica
Pubblicato in: https://hal.science/tel-04407139, Numero 1, 2023
Editore: Institut Polytechnique de Paris

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

Autori: Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi
Pubblicato in: Lecture Notes in Computer Science, Foundations and Practice of Security, 2024, Pagina/e 440-458, ISBN 978-3-031-57537-2
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-57537-2_27

Establishing the Price of Privacy in Federated Data Trading

Autori: Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi
Pubblicato in: Protocols, Strands, and Logic, 2021, ISBN 978-3-030-91631-2
Editore: Springer
DOI: 10.1007/978-3-030-91631-2_13

Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy

Autori: Falaschi, Moreno; Palamidessi, Catuscia; Romanelli, Marco
Pubblicato in: "Recent Developments in the Design and Implementation of Programming Languages, 86, Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, pp.11:1-11:20, 2020, OASICS, ⟨10.4230/OASIcs.Gabbrielli.2020.11⟩", Numero 86, 2020, Pagina/e 11:1--11:20
Editore: Schloss Dagstuhl--Leibniz-Zentrum fur Informatik
DOI: 10.4230/oasics.gabbrielli.11

The Science of Quantitative Information Flow

Autori: Mário S. Alvim, Konstantinos Chatzikokolakis, Annabelle McIver, Carroll Morgan, Catuscia Palamidessi, Geoffrey Smith
Pubblicato in: 2020, ISBN 978-3-319-96131-6
Editore: Springer
DOI: 10.1007/978-3-319-96131-6

Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond

Autori: Filippo Galli; Kangsoo Jung; Sayan Biswas; Catuscia Palamidessi; Tommaso Cucinotta
Pubblicato in: https://link.springer.com/journal/42979/volumes-and-issues/4-6, Numero Springer Nature Computer Science, 2023, ISSN 2661-8907
Editore: Springer
DOI: 10.1007/s42979-023-02292-0

Bounding information leakage in machine learning

Autori: Del Grosso, Ganesh; Pichler, George; Palamidessi, Catuscia; Piantanida, Pablo
Pubblicato in: Neurocomputing, 2023, ISSN 0925-2312
Editore: Elsevier BV
DOI: 10.1016/j.neucom.2023.02.058

Machine learning fairness notions: Bridging the gap with real-world applications

Autori: Karima Makhlouf; Sami Zhioua; Catuscia Palamidessi
Pubblicato in: Information Processing and Management, Numero 58, 2021, ISSN 0306-4573
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.ipm.2021.102642

Online Sensitivity Optimization in Differentially Private Learning

Autori: Filippo Galli, Catuscia Palamidessi, Tommaso Cucinotta
Pubblicato in: Proceedings of the AAAI Conference on Artificial Intelligence, Numero 38, 2024, Pagina/e 12109-12117, ISSN 2374-3468
Editore: OJS/PKP
DOI: 10.1609/aaai.v38i11.29099

On the Applicability of Machine Learning Fairness Notions

Autori: Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi
Pubblicato in: ACM SIGKDD Explorations Newsletter, Numero 23/1, 2021, Pagina/e 14-23, ISSN 1931-0145
Editore: Association for Computing Machinery
DOI: 10.1145/3468507.3468511

Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates

Autori: Héber H. Arcolezi, Jean-François Couchot, Bechara Al Bouna, Xiaokui Xiao
Pubblicato in: Digital Communications and Networks, Numero 10, 2024, Pagina/e 369-379, ISSN 2352-8648
Editore: Elsevier B.V.
DOI: 10.1016/j.dcan.2022.07.003

On the incompatibility of accuracy and equal opportunity

Autori: Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida, Frank Valencia
Pubblicato in: Machine Learning, Numero 113, 2024, Pagina/e 2405-2434, ISSN 0885-6125
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10994-023-06331-y

Gender and sex bias in COVID-19 epidemiological data through the lens of causality

Autori: Natalia Díaz-Rodríguez; Rūta Binkytė; Wafae Bakkali; Sannidhi Bookseller; Paola Tubaro; Andrius Bacevičius; Sami Zhioua; Raja Chatila
Pubblicato in: Information Processing and Management, Numero Volume 60, Numero 3, 2023, ISSN 0306-4573
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.ipm.2023.103276

Enhanced models for privacy and utility in continuous-time diffusion networks

Autori: Federica Granese, Daniele Gorla, Catuscia Palamidessi
Pubblicato in: International Journal of Information Security, 2021, ISSN 1615-5262
Editore: Springer Verlag
DOI: 10.1007/s10207-020-00530-7

Survey on fairness notions and related tensions

Autori: Guilherme Alves, Fabien Bernier, Miguel Couceiro, Karima Makhlouf, Catuscia Palamidessi, Sami Zhioua
Pubblicato in: EURO Journal on Decision Processes, Numero 11, 2023, Pagina/e 100033, ISSN 2352-2208
Editore: Elsevier Ltd
DOI: 10.1016/j.ejdp.2023.100033

On the Impact of Multi-dimensional Local Differential Privacy on Fairness

Autori: Makhlouf, Karima; Hwang Arcolezi, Héber; Zhioua, Sami; Brahim, Ghassen, Ben; Palamidessi, Catuscia
Pubblicato in: Data Mining and Knowledge Discovery, 2024, ISSN 0167-6423
Editore: Elsevier BV
DOI: 10.1007/s10618-024-01031-0

A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection

Autori: Picot, Marine; Granese, Federica; Staerman, Guillaume; Romanelli, Marco; Messina, Francisco; Piantanida, Pablo; Colombo, Pierre
Pubblicato in: Transactions on Machine Learning Research, 2023, ISSN 2380-5382
Editore: OpenReview

BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables

Autori: Binkyte, Ruta; Gorla, Daniele; Palamidessi, Catuscia
Pubblicato in: FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024, ISSN 1615-5262
Editore: Springer Verlag
DOI: 10.1145/3630106.3659016

On the Risks of Collecting Multidimensional Data Under Local Differential Privacy

Autori: Arcolezi, Héber, H.; Gambs, Sébastien; Couchot, Jean-François; Palamidessi, Catuscia
Pubblicato in: Proceedings of the VLDB Endowment (PVLDB), 2023, ISSN 2150-8097
Editore: VLDB Endowment
DOI: 10.14778/3579075.3579086

When Causality Meets Fairness: A Survey

Autori: Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi
Pubblicato in: Journal of Logical and Algebraic Methods in Programming, Numero 141, 2024, Pagina/e 101000, ISSN 2352-2208
Editore: Elsevier
DOI: 10.2139/ssrn.4641132

Refinement Orders for Quantitative Information Flow and Differential Privacy

Autori: Konstantinos Chatzikokolakis; Natasha Fernandes; Catuscia Palamidessi
Pubblicato in: Journal of Cybersecurity and Privacy, Numero 1, 2022, ISSN 2624-800X
Editore: MDPI
DOI: 10.3390/jcp1010004

A logical characterization of differential privacy

Autori: Valentina Castiglioni, Konstantinos Chatzikokolakis, Catuscia Palamidessi
Pubblicato in: Science of Computer Programming, Numero 188, 2020, Pagina/e 102388, ISSN 0167-6423
Editore: Elsevier BV
DOI: 10.1016/j.scico.2019.102388

Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?

Autori: Héber Hwang Arcolezi, Jean-François Couchot, Denis Renaud, Bechara Al Bouna, Xiaokui Xiao
Pubblicato in: Neural Computing and Applications, Numero 34, 2024, Pagina/e 13355-13369, ISSN 0941-0643
Editore: Springer Verlag
DOI: 10.1007/s00521-022-07393-0

Information Leakage Games: Exploring Information as a Utility Function

Autori: Catuscia Palamidessi; Mário Alvim; Yusuke Kawamoto; Konstantinos Chatzikokolakis
Pubblicato in: ACM Transactions on Privacy and Security, Numero 25, 2022, ISSN 2471-2566
Editore: ACM
DOI: 10.48550/arxiv.2012.12060

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