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ORCIDMilan Lopuhaä-Zwakenberg
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2020 – today
- 2025
[j4]Wenrui Yu, Qiongxiu Li, Milan Lopuhaä-Zwakenberg, Mads Græsbøll Christensen, Richard Heusdens:
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization. IEEE Trans. Inf. Forensics Secur. 20: 822-838 (2025)- 2024
- [j3]Milan Lopuhaä-Zwakenberg
, Jasper Goseling:
Mechanisms for Robust Local Differential Privacy. Entropy 26(3): 233 (2024) - [j2]Qiongxiu Li, Jaron Skovsted Gundersen, Milan Lopuhaä-Zwakenberg, Richard Heusdens:
Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus. IEEE Trans. Inf. Forensics Secur. 19: 1780-1793 (2024) - [c18]Milan Lopuhaä-Zwakenberg:
Attack Tree Metrics are Operad Algebras. CSF 2024: 665-679 - [c17]Qiongxiu Li, Milan Lopuhaä-Zwakenberg, Wenrui Yu, Richard Heusdens:
On the Privacy Bound of Distributed Optimization and its Application in Federated Learning. EUSIPCO 2024: 2232-2236 - [c16]Thi Kim Nhung Dang, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Fuzzy quantitative attack tree analysis. FASE 2024: 210-231 - [c15]Milan Lopuhaä-Zwakenberg:
Fault Tree Reliability Analysis via Squarefree Polynomials. MODELSWARD 2024: 39-49 - [c14]Reza Soltani, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Safety-Security Analysis via Attack-Fault-Defense Trees: Semantics and Cut Set Metrics. SAFECOMP 2024: 218-232 - [i22]Milan Lopuhaä-Zwakenberg:
Attack tree metrics are operad algebras. CoRR abs/2401.10008 (2024) - [i21]Thi Kim Nhung Dang, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Fuzzy quantitative attack tree analysis. CoRR abs/2401.12346 (2024) - [i20]Stefano M. Nicoletti, Milan Lopuhaä-Zwakenberg, Ernst Moritz Hahn, Mariëlle Stoelinga:
Querying Fault and Attack Trees: Property Specification on a Water Network. CoRR abs/2401.16015 (2024) - [i19]Thi Kim Nhung Dang, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Fuzzy Fault Trees Formalized. CoRR abs/2403.08843 (2024) - [i18]Wenrui Yu, Qiongxiu Li, Milan Lopuhaä-Zwakenberg, Mads Græsbøll Christensen, Richard Heusdens:
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization. CoRR abs/2407.09324 (2024) - [i17]Milan Lopuhaä-Zwakenberg:
Quantitative analysis of attack-fault trees via Markov decision processes. CoRR abs/2408.06914 (2024) - [i16]Stefano M. Nicoletti, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga, Fabio Massacci, Carlos E. Budde:
How hard can it be? Quantifying MITRE attack campaigns with attack trees and cATM logic. CoRR abs/2410.06692 (2024) - 2023
- [j1]Milan Lopuhaä-Zwakenberg, Carlos E. Budde, Mariëlle Stoelinga:
Efficient and Generic Algorithms for Quantitative Attack Tree Analysis. IEEE Trans. Dependable Secur. Comput. 20(5): 4169-4187 (2023) - [c13]Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Cost-Damage Analysis of Attack Trees. DSN 2023: 545-558 - [c12]Stefano M. Nicoletti, Milan Lopuhaä-Zwakenberg, Ernst Moritz Hahn, Mariëlle Stoelinga:
sfPFL: A Probabilistic Logic for Fault Trees. FM 2023: 199-221 - [c11]Reza Soltani, Matthias Volk, Leonardo Diamonte, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Optimal Spare Management via Statistical Model Checking: A Case Study in Research Reactors. FMICS 2023: 205-223 - [c10]Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Attack Time Analysis in Dynamic Attack Trees via Integer Linear Programming. SEFM 2023: 165-183 - [c9]Stefano M. Nicoletti, Milan Lopuhaä-Zwakenberg, Ernst Moritz Hahn, Mariëlle Stoelinga:
sfATM: A Logic for Quantitative Security Properties on Attack Trees. SEFM 2023: 205-225 - [i15]Stefano M. Nicoletti, Milan Lopuhaä-Zwakenberg, Ernst Moritz Hahn, Mariëlle Stoelinga:
PFL: a Probabilistic Logic for Fault Trees. CoRR abs/2303.17314 (2023) - [i14]Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Cost-damage analysis of attack trees. CoRR abs/2304.05812 (2023) - [i13]Stefano M. Nicoletti, Milan Lopuhaä-Zwakenberg, Ernst Moritz Hahn, Mariëlle Stoelinga:
ATM: a Logic for Quantitative Security Properties on Attack Trees. CoRR abs/2309.09231 (2023) - [i12]Milan Lopuhaä-Zwakenberg:
Fault tree reliability analysis via squarefree polynomials. CoRR abs/2312.05836 (2023) - [i11]Qiongxiu Li, Jaron Skovsted Gundersen, Milan Lopuhaä-Zwakenberg, Richard Heusdens:
Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus. CoRR abs/2312.07947 (2023) - 2022
- [c8]Qiongxiu Li, Milan Lopuhaä-Zwakenberg, Richard Heusdens, Mads Græsbøll Christensen:
Two for the price of one: communication efficient and privacy-preserving distributed average consensus using quantization. EUSIPCO 2022: 2166-2170 - [c7]Jasper Goseling, Milan Lopuhaä-Zwakenberg:
Robust Optimization for Local Differential Privacy. ISIT 2022: 1629-1634 - [c6]Milan Lopuhaä-Zwakenberg, Boris Skoric, Ninghui Li:
Fisher Information as a Utility Metric for Frequency Estimation under Local Differential Privacy. WPES@CCS 2022: 41-53 - [i10]Jasper Goseling, Milan Lopuhaä-Zwakenberg:
Robust Optimization for Local Differential Privacy. CoRR abs/2205.05015 (2022) - [i9]Milan Lopuhaä-Zwakenberg, Carlos E. Budde, Mariëlle Stoelinga:
Efficient and Generic Algorithms for Quantitative Attack Tree Analysis. CoRR abs/2212.05358 (2022) - 2021
- [c5]Milan Lopuhaä-Zwakenberg, Jasper Goseling:
Robust Local Differential Privacy. ISIT 2021: 557-562 - [c4]Milan Lopuhaä-Zwakenberg, Mina Alishahi, Jeroen Kivits, Jordi Klarenbeek, Gert-Jan van der Velde, Nicola Zannone:
Comparing Classifiers' Performance under Differential Privacy. SECRYPT 2021: 50-61 - [i8]Milan Lopuhaä-Zwakenberg, Jasper Goseling:
The Privacy-Utility Tradeoff of Robust Local Differential Privacy. CoRR abs/2101.09139 (2021) - [i7]Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga:
Attack time analysis in dynamic attack trees via integer linear programming. CoRR abs/2111.05114 (2021) - 2020
- [c3]Tianhao Wang, Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Skoric, Ninghui Li:
Locally Differentially Private Frequency Estimation with Consistency. NDSS 2020 - [c2]Zitao Li, Tianhao Wang, Milan Lopuhaä-Zwakenberg, Ninghui Li, Boris Skoric:
Estimating Numerical Distributions under Local Differential Privacy. SIGMOD Conference 2020: 621-635 - [c1]Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Skoric, Ninghui Li:
Improving Frequency Estimation under Local Differential Privacy. WPES@CCS 2020: 123-135 - [i6]Milan Lopuhaä-Zwakenberg:
The Privacy Funnel from the viewpoint of Local Differential Privacy. CoRR abs/2002.01501 (2020) - [i5]Milan Lopuhaä-Zwakenberg, Haochen Tong, Boris Skoric:
Data Sanitisation Protocols for the Privacy Funnel with Differential Privacy Guarantees. CoRR abs/2008.13151 (2020)
2010 – 2019
- 2019
- [i4]Tianhao Wang, Zitao Li, Ninghui Li, Milan Lopuhaä-Zwakenberg, Boris Skoric:
Consistent and Accurate Frequency Oracles under Local Differential Privacy. CoRR abs/1905.08320 (2019) - [i3]Milan Lopuhaä-Zwakenberg, Boris Skoric, Ninghui Li:
Information-theoretic metrics for Local Differential Privacy protocols. CoRR abs/1910.07826 (2019) - [i2]Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Skoric, Ninghui Li:
Four accuracy bounds and one estimator for frequency estimation under local differential privacy. CoRR abs/1911.10499 (2019) - [i1]Zitao Li, Tianhao Wang, Milan Lopuhaä-Zwakenberg, Boris Skoric, Ninghui Li:
Estimating Numerical Distributions under Local Differential Privacy. CoRR abs/1912.01051 (2019)
Coauthor Index
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last updated on 2025-01-27 00:42 CET by the dblp team
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