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ORCIDCatuscia Palamidessi
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- affiliation: École Polytechnique, Paris, France
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2020 – today
- 2024
[j81]Karima Makhlouf, Héber Hwang Arcolezi, Sami Zhioua, Ghassen Ben Brahim, Catuscia Palamidessi:
On the impact of multi-dimensional local differential privacy on fairness. Data Min. Knowl. Discov. 38(4): 2252-2275 (2024)- [j80]Karima Makhlouf
, Sami Zhioua, Catuscia Palamidessi:
When causality meets fairness: A survey. J. Log. Algebraic Methods Program. 141: 101000 (2024) - [j79]Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida, Frank Valencia:
On the incompatibility of accuracy and equal opportunity. Mach. Learn. 113(5): 2405-2434 (2024) - [j78]Sayan Biswas, Catuscia Palamidessi:
PRIVIC: A privacy-preserving method for incremental collection of location data. Proc. Priv. Enhancing Technol. 2024(1): 582-596 (2024) - [j77]Catuscia Palamidessi:
Chair's Letter. ACM SIGLOG News 11(2): 2 (2024) - [j76]Catuscia Palamidessi:
Chair's Letter. ACM SIGLOG News 11(3): 2 (2024) - [c172]Filippo Galli, Catuscia Palamidessi, Tommaso Cucinotta:
Online Sensitivity Optimization in Differentially Private Learning. AAAI 2024: 12109-12117 - [c171]Ruta Binkyte, Carlos Antonio Pinzóon, Szilvia Lestyan, Kangsoo Jung, Héber Hwang Arcolezi, Catuscia Palamidessi:
Causal Discovery Under Local Privacy. CLeaR 2024: 325-383 - [c170]Karima Makhlouf, Tamara Stefanovic, Héber Hwang Arcolezi, Catuscia Palamidessi:
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results. CSF 2024: 1-16 - [c169]Ruta Binkyte, Daniele Gorla, Catuscia Palamidessi:
BaBE: Enhancing Fairness via Estimation of Explaining Variables. FAccT 2024: 1917-1925 - [i63]Karima Makhlouf, Tamara Stefanovic, Héber Hwang Arcolezi, Catuscia Palamidessi:
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results. CoRR abs/2405.14725 (2024) - [i62]Sayan Biswas, Mark Dras, Pedro Faustini, Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Parastoo Sadeghi:
Bayes' capacity as a measure for reconstruction attacks in federated learning. CoRR abs/2406.13569 (2024) - 2023
- [j75]Ganesh Del Grosso, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida:
Bounding information leakage in machine learning. Neurocomputing 534: 1-17 (2023) - [j74]Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Ming Ding:
Universal optimality and robust utility bounds for metric differential privacy. J. Comput. Secur. 31(5): 539-580 (2023) - [j73]Héber Hwang Arcolezi, Sébastien Gambs, Jean-François Couchot, Catuscia Palamidessi:
On the Risks of Collecting Multidimensional Data Under Local Differential Privacy. Proc. VLDB Endow. 16(5): 1126-1139 (2023) - [j72]Catuscia Palamidessi:
Chair's Letter. ACM SIGLOG News 10(1): 2 (2023) - [j71]Filippo Galli, Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi, Tommaso Cucinotta:
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond. SN Comput. Sci. 4(6): 831 (2023) - [c168]Catuscia Palamidessi:
Local Methods for Privacy Protection and Impact on Fairness. CODASPY 2023: 103 - [c167]Konstantinos Chatzikokolakis, Giovanni Cherubin, Catuscia Palamidessi, Carmela Troncoso:
Bayes Security: A Not So Average Metric. CSF 2023: 388-406 - [c166]Mireya Jurado, Ramon G. Gonze, Mário S. Alvim, Catuscia Palamidessi:
Analyzing the Shuffle Model Through the Lens of Quantitative Information Flow. CSF 2023: 423-438 - [c165]Héber Hwang Arcolezi, Karima Makhlouf, Catuscia Palamidessi:
(Local) Differential Privacy has NO Disparate Impact on Fairness. DBSec 2023: 3-21 - [c164]Héber Hwang Arcolezi, Selene Cerna, Catuscia Palamidessi:
On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms. DBSec 2023: 165-183 - [c163]Héber Hwang Arcolezi, Carlos Pinzón, Catuscia Palamidessi, Sébastien Gambs:
Frequency Estimation of Evolving Data Under Local Differential Privacy. EDBT 2023: 512-525 - [c162]Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi:
Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response. FPS (1) 2023: 440-458 - [c161]Filippo Galli, Sayan Biswas, Kangsoo Jung, Tommaso Cucinotta, Catuscia Palamidessi:
Group Privacy for Personalized Federated Learning. ICISSP 2023: 252-263 - [c160]Selene Cerna, Catuscia Palamidessi:
On the application and impact of ε-DP and fairness in ambulance engagement time prediction. Tiny Papers @ ICLR 2023 - [c159]Daniele Gorla, Louis Jalouzot, Federica Granese, Catuscia Palamidessi, Pablo Piantanida:
On the (Im)Possibility of Estimating Various Notions of Differential Privacy (short paper). ICTCS 2023: 219-224 - [c158]Sebastian Simon, Cezara Petrui, Carlos Pinzón, Catuscia Palamidessi:
Obfuscation Padding Schemes that Minimize Rényi Min-Entropy for Privacy. ISPEC 2023: 74-90 - [i61]Héber Hwang Arcolezi, Karima Makhlouf, Catuscia Palamidessi:
(Local) Differential Privacy has NO Disparate Impact on Fairness. CoRR abs/2304.12845 (2023) - [i60]Mireya Jurado, Ramon G. Gonze, Mário S. Alvim, Catuscia Palamidessi:
Analyzing the Shuffle Model through the Lens of Quantitative Information Flow. CoRR abs/2305.13075 (2023) - [i59]Ruta Binkyte, Daniele Gorla, Catuscia Palamidessi:
BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables. CoRR abs/2307.02891 (2023) - [i58]Héber Hwang Arcolezi, Selene Cerna, Catuscia Palamidessi:
On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms. CoRR abs/2307.07744 (2023) - [i57]Filippo Galli, Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi, Tommaso Cucinotta:
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond. CoRR abs/2309.00416 (2023) - [i56]Filippo Galli, Catuscia Palamidessi, Tommaso Cucinotta:
Online Sensitivity Optimization in Differentially Private Learning. CoRR abs/2310.00829 (2023) - [i55]Ruta Binkyte, Carlos Pinzón, Szilvia Lestyán, Kangsoo Jung, Héber Hwang Arcolezi, Catuscia Palamidessi:
Causal Discovery Under Local Privacy. CoRR abs/2311.04037 (2023) - [i54]Karima Makhlouf, Héber Hwang Arcolezi, Sami Zhioua, Ghassen Ben Brahim, Catuscia Palamidessi:
On the Impact of Multi-dimensional Local Differential Privacy on Fairness. CoRR abs/2312.04404 (2023) - 2022
- [j70]Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto, Catuscia Palamidessi:
Information Leakage Games: Exploring Information as a Utility Function. ACM Trans. Priv. Secur. 25(3): 20:1-20:36 (2022) - [c157]Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida, Frank Valencia:
On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints. AAAI 2022: 7993-8000 - [c156]Ruta Binkyte, Karima Makhlouf, Carlos Pinzón, Sami Zhioua, Catuscia Palamidessi:
Causal Discovery for Fairness. AFCP 2022: 7-22 - [c155]Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Ming Ding:
Universal Optimality and Robust Utility Bounds for Metric Differential Privacy. CSF 2022: 348-363 - [c154]Ganesh Del Grosso, Hamid Jalalzai, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida:
Leveraging Adversarial Examples to Quantify Membership Information Leakage. CVPR 2022: 10389-10399 - [c153]Héber Hwang Arcolezi, Jean-François Couchot, Sébastien Gambs, Catuscia Palamidessi, Majid Zolfaghari:
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python. ESORICS (3) 2022: 770-775 - [i53]Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi:
Identifiability of Causal-based Fairness Notions: A State of the Art. CoRR abs/2203.05900 (2022) - [i52]Ganesh Del Grosso, Hamid Jalalzai, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida:
Leveraging Adversarial Examples to Quantify Membership Information Leakage. CoRR abs/2203.09566 (2022) - [i51]Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Ming Ding:
Universal Optimality and Robust Utility Bounds for Metric Differential Privacy. CoRR abs/2205.01258 (2022) - [i50]Héber Hwang Arcolezi, Jean-François Couchot, Sébastien Gambs, Catuscia Palamidessi, Majid Zolfaghari:
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python. CoRR abs/2205.02648 (2022) - [i49]Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi:
Tight Differential Privacy Blanket for Shuffle Model. CoRR abs/2205.04410 (2022) - [i48]Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi:
Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response. CoRR abs/2205.08858 (2022) - [i47]Ugur-Ilker Atmaca, Sayan Biswas, Carsten Maple, Catuscia Palamidessi:
A privacy preserving querying mechanism with high utility for electric vehicles. CoRR abs/2206.02060 (2022) - [i46]Filippo Galli, Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi, Tommaso Cucinotta:
Group privacy for personalized federated learning. CoRR abs/2206.03396 (2022) - [i45]Ruta Binkyte-Sadauskiene, Karima Makhlouf, Carlos Pinzón, Sami Zhioua, Catuscia Palamidessi:
Causal Discovery for Fairness. CoRR abs/2206.06685 (2022) - [i44]Sayan Biswas, Catuscia Palamidessi:
Three-way optimization of privacy and utility of location data. CoRR abs/2206.10525 (2022) - [i43]Ehab ElSalamouny, Catuscia Palamidessi:
Reconstruction of the distribution of sensitive data under free-will privacy. CoRR abs/2208.11268 (2022) - [i42]Daniele Gorla, Louis Jalouzot, Federica Granese, Catuscia Palamidessi, Pablo Piantanida:
On the (Im)Possibility of Estimating Various Notions of Differential Privacy. CoRR abs/2208.14414 (2022) - [i41]Héber Hwang Arcolezi, Sébastien Gambs, Jean-François Couchot, Catuscia Palamidessi:
On the Risks of Collecting Multidimensional Data Under Local Differential Privacy. CoRR abs/2209.01684 (2022) - [i40]Sebastian Simon, Cezara Petrui, Carlos Pinzón, Catuscia Palamidessi:
Minimizing Information Leakage under Padding Constraints. CoRR abs/2209.04379 (2022) - [i39]Guilherme Alves, Fabien Bernier, Miguel Couceiro, Karima Makhlouf, Catuscia Palamidessi, Sami Zhioua:
Survey on Fairness Notions and Related Tensions. CoRR abs/2209.13012 (2022) - [i38]Héber Hwang Arcolezi, Carlos Pinzón, Catuscia Palamidessi, Sébastien Gambs:
Frequency Estimation of Evolving Data Under Local Differential Privacy. CoRR abs/2210.00262 (2022) - 2021
- [j69]Federica Granese, Daniele Gorla, Catuscia Palamidessi:
Enhanced models for privacy and utility in continuous-time diffusion networks. Int. J. Inf. Sec. 20(5): 763-782 (2021) - [j68]Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi:
Machine learning fairness notions: Bridging the gap with real-world applications. Inf. Process. Manag. 58(5): 102642 (2021) - [j67]Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi:
On the Applicability of Machine Learning Fairness Notions. SIGKDD Explor. 23(1): 14-23 (2021) - [c152]Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi:
Establishing the Price of Privacy in Federated Data Trading. Protocols, Strands, and Logic 2021: 232-250 - [c151]Nathalie Bertrand, Luca de Alfaro, Rob van Glabbeek, Catuscia Palamidessi, Nobuko Yoshida:
CONCUR Test-Of-Time Award 2021 (Invited Paper). CONCUR 2021: 1:1-1:3 - [c150]Mireya Jurado, Catuscia Palamidessi, Geoffrey Smith:
A Formal Information-Theoretic Leakage Analysis of Order-Revealing Encryption. CSF 2021: 1-16 - [c149]Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi:
An Incentive Mechanism for Trading Personal Data in Data Markets. ICTAC 2021: 197-213 - [c148]Abhishek Kumar Mishra, Aline Carneiro Viana, Nadjib Achir, Catuscia Palamidessi:
Public Wireless Packets Anonymously Hurt You. LCN 2021: 649-652 - [c147]Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida:
DOCTOR: A Simple Method for Detecting Misclassification Errors. NeurIPS 2021: 5669-5681 - [i37]Ganesh Del Grosso, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida:
Bounding Information Leakage in Machine Learning. CoRR abs/2105.03875 (2021) - [i36]Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida:
DOCTOR: A Simple Method for Detecting Misclassification Errors. CoRR abs/2106.02395 (2021) - [i35]Sayan Biswas, Kangsoo Jung, Catuscia Palamidessi:
An Incentive Mechanism for Trading Personal Data in Data Markets. CoRR abs/2106.14187 (2021) - [i34]Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida, Frank Valencia:
On the impossibility of non-trivial accuracy under fairness constraints. CoRR abs/2107.06944 (2021) - [i33]Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi:
Establishing the Price of Privacy in Federated Data Trading. CoRR abs/2111.15415 (2021) - 2020
- [b1]Mário S. Alvim, Konstantinos Chatzikokolakis, Annabelle McIver, Carroll Morgan, Catuscia Palamidessi, Geoffrey Smith:
The Science of Quantitative Information Flow. Information Security and Cryptography, Springer 2020, ISBN 978-3-319-96129-3, pp. I-XXVIII, 1-478 - [j66]Moreno Falaschi, Maurizio Gabbrielli, Carlos Olarte, Catuscia Palamidessi:
Dynamic Slicing for Concurrent Constraint Languages. Fundam. Informaticae 177(3-4): 331-357 (2020) - [j65]Konstantinos Chatzikokolakis, Natasha Fernandes, Catuscia Palamidessi:
Refinement Orders for Quantitative Information Flow and Differential Privacy. J. Cybersecur. Priv. 1(1): 40-77 (2020) - [j64]Valentina Castiglioni, Konstantinos Chatzikokolakis, Catuscia Palamidessi:
A logical characterization of differential privacy. Sci. Comput. Program. 188: 102388 (2020) - [c146]Moreno Falaschi, Catuscia Palamidessi, Marco Romanelli:
Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy. Gabbrielli's Festschrift 2020: 11:1-11:20 - [c145]Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi, Pablo Piantanida:
Estimating g-Leakage via Machine Learning. CCS 2020: 697-716 - [c144]Catuscia Palamidessi, Marco Romanelli:
Modern Applications of Game-Theoretic Principles (Invited Paper). CONCUR 2020: 4:1-4:9 - [c143]Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi:
Optimal Obfuscation Mechanisms via Machine Learning. CSF 2020: 153-168 - [c142]Ehab ElSalamouny, Catuscia Palamidessi:
Generalized Iterative Bayesian Update and Applications to Mechanisms for Privacy Protection. EuroS&P 2020: 490-507 - [i32]Catuscia Palamidessi, Marco Romanelli:
Feature selection in machine learning: Rényi min-entropy vs Shannon entropy. CoRR abs/2001.09654 (2020) - [i31]Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi, Pablo Piantanida:
Estimating g-Leakage via Machine Learning. CoRR abs/2005.04399 (2020) - [i30]Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi:
On the Applicability of ML Fairness Notions. CoRR abs/2006.16745 (2020) - [i29]Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi:
Survey on Causal-based Machine Learning Fairness Notions. CoRR abs/2010.09553 (2020) - [i28]Konstantinos Chatzikokolakis, Giovanni Cherubin, Catuscia Palamidessi, Carmela Troncoso:
The Bayes Security Measure. CoRR abs/2011.03396 (2020) - [i27]Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto, Catuscia Palamidessi:
Information Leakage Games: Exploring Information as a Utility Function. CoRR abs/2012.12060 (2020)
2010 – 2019
- 2019
- [j63]Mário S. Alvim, Konstantinos Chatzikokolakis, Annabelle McIver, Carroll Morgan, Catuscia Palamidessi, Geoffrey Smith:
An axiomatization of information flow measures. Theor. Comput. Sci. 777: 32-54 (2019) - [c141]Natasha Fernandes, Lefki Kacem, Catuscia Palamidessi:
Utility-Preserving Privacy Mechanisms for Counting Queries. Models, Languages, and Tools for Concurrent and Distributed Programming 2019: 487-495 - [c140]Konstantinos Chatzikokolakis, Natasha Fernandes, Catuscia Palamidessi:
Comparing Systems: Max-Case Refinement Orders and Application to Differential Privacy. CSF 2019: 442-457 - [c139]Adriano Di Luzio, Aline Carneiro Viana, Konstantinos Chatzikokolakis, Georgi Dikov, Catuscia Palamidessi, Julinda Stefa:
Catch me if you can: how geo-indistinguishability affects utility in mobility-based geographic datasets. LocalRec@SIGSPATIAL 2019: 8:1-8:10 - [c138]Daniele Gorla, Federica Granese, Catuscia Palamidessi:
Enhanced Models for Privacy and Utility in Continuous-Time Diffusion Networks. ICTAC 2019: 313-331 - [c137]Ali Kassem, Gergely Ács, Claude Castelluccia, Catuscia Palamidessi:
Differential Inference Testing: A Practical Approach to Evaluate Sanitizations of Datasets. IEEE Symposium on Security and Privacy Workshops 2019: 72-79 - [c136]Giovanni Cherubin, Konstantinos Chatzikokolakis, Catuscia Palamidessi:
F-BLEAU: Fast Black-Box Leakage Estimation. IEEE Symposium on Security and Privacy 2019: 835-852 - [i26]Giovanni Cherubin, Konstantinos Chatzikokolakis, Catuscia Palamidessi:
F-BLEAU: Fast Black-box Leakage Estimation. CoRR abs/1902.01350 (2019) - [i25]Marco Romanelli, Catuscia Palamidessi, Konstantinos Chatzikokolakis:
Generating Optimal Privacy-Protection Mechanisms via Machine Learning. CoRR abs/1904.01059 (2019) - [i24]Natasha Fernandes, Lefki Kacem, Catuscia Palamidessi:
Utility-Preserving Privacy Mechanisms for Counting Queries. CoRR abs/1906.12147 (2019) - [i23]Ehab ElSalamouny, Catuscia Palamidessi:
Full Convergence of the Iterative Bayesian Update and Applications to Mechanisms for Privacy Protection. CoRR abs/1909.02961 (2019) - 2018
- [j62]Thomas Eiter, Javier Esparza, Catuscia Palamidessi, Gordon D. Plotkin, Natarajan Shankar:
Alonzo Church Award 2018 - Call for Nominations. Bull. EATCS 124 (2018) - [j61]Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto, Catuscia Palamidessi:
A Game-Theoretic Approach to Information-Flow Control via Protocol Composition. Entropy 20(5): 382 (2018) - [c135]Catuscia Palamidessi, Marco Romanelli:
Feature Selection with Rényi Min-Entropy. ANNPR 2018: 226-239 - [c134]Lefki Kacem, Catuscia Palamidessi:
Geometric Noise for Locally Private Counting Queries. PLAS@CCS 2018: 13-16 - [c133]Mário S. Alvim, Konstantinos Chatzikokolakis, Catuscia Palamidessi, Anna Pazii:
Invited Paper: Local Differential Privacy on Metric Spaces: Optimizing the Trade-Off with Utility. CSF 2018: 262-267 - [c132]Valentina Castiglioni, Konstantinos Chatzikokolakis, Catuscia Palamidessi:
A Logical Characterization of Differential Privacy via Behavioral Metrics. FACS 2018: 75-96 - [c131]Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto, Catuscia Palamidessi:
Leakage and Protocol Composition in a Game-Theoretic Perspective. POST 2018: 134-159 - [i22]Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto, Catuscia Palamidessi:
Leakage and Protocol Composition in a Game-Theoretic Perspective. CoRR abs/1802.10465 (2018) - [i21]Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto, Catuscia Palamidessi:
A Game-Theoretic Approach to Information-Flow Control via Protocol Composition. CoRR abs/1803.10042 (2018) - [i20]Mário S. Alvim, Konstantinos Chatzikokolakis, Catuscia Palamidessi, Anna Pazii:
Metric-based local differential privacy for statistical applications. CoRR abs/1805.01456 (2018) - 2017
- [j60]Natarajan Shankar, Catuscia Palamidessi, Gordon D. Plotkin, Moshe Y. Vardi:
Alonzo Church Award 2017 - Call for Nominations. Bull. EATCS 121 (2017) - [j59]Konstantinos Chatzikokolakis, Ehab ElSalamouny, Catuscia Palamidessi, Anna Pazii:
Methods for Location Privacy: A comparative overview. Found. Trends Priv. Secur. 1(4): 199-257 (2017) - [j58]Sardaouna Hamadou, Catuscia Palamidessi, Vladimiro Sassone:
Quantifying leakage in the presence of unreliable sources of information. J. Comput. Syst. Sci. 88: 27-52 (2017) - [j57]Yusuke Kawamoto, Konstantinos Chatzikokolakis, Catuscia Palamidessi:
On the Compositionality of Quantitative Information Flow. Log. Methods Comput. Sci. 13(3) (2017) - [j56]Konstantinos Chatzikokolakis, Ehab ElSalamouny, Catuscia Palamidessi:
Efficient Utility Improvement for Location Privacy. Proc. Priv. Enhancing Technol. 2017(4): 308-328 (2017) - [c130]Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto, Catuscia Palamidessi:
Information Leakage Games. GameSec 2017: 437-457 - [c129]Konstantinos Chatzikokolakis, Serge Haddad, Ali Kassem, Catuscia Palamidessi:
Trading Optimality for Performance in Location Privacy. VALUETOOLS 2017: 221-222 - [i19]Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto, Catuscia Palamidessi:
Information Leakage Games. CoRR abs/1705.05030 (2017) - [i18]Konstantinos Chatzikokolakis, Serge Haddad, Ali Kassem, Catuscia Palamidessi:
Trading Optimality for Performance in Location Privacy. CoRR abs/1710.05524 (2017) - 2016
- [j55]Konstantinos Chatzikokolakis, Catuscia Palamidessi, Christelle Braun:
Compositional methods for information-hiding. Math. Struct. Comput. Sci. 26(6): 908-932 (2016)