Luttermann, Malte; Machemer, Johann; Gehrke, Marcel
Efficient Detection of Commutative Factors in Factor Graphs Proceedings Article
In: Kwisthout, Johan; Renooij, Silja (Hrsg.): Proceedings of The 12th International Conference on Probabilistic Graphical Models, S. 38–56, PMLR, 2024.
@inproceedings{pmlr-v246-luttermann24a,
title = {Efficient Detection of Commutative Factors in Factor Graphs},
author = {Malte Luttermann and Johann Machemer and Marcel Gehrke},
editor = {Johan Kwisthout and Silja Renooij},
url = {https://proceedings.mlr.press/v246/luttermann24a.html},
year = {2024},
date = {2024-09-01},
booktitle = {Proceedings of The 12th International Conference on Probabilistic Graphical Models},
volume = {246},
pages = {38–56},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
abstract = {Lifted probabilistic inference exploits symmetries in probabilistic graphical models to allow for tractable probabilistic inference with respect to domain sizes. To exploit symmetries in, e.g., factor graphs, it is crucial to identify commutative factors, i.e., factors having symmetries within themselves due to their arguments being exchangeable. The current state-of-the-art to check whether a factor is commutative with respect to a subset of its arguments iterates over all possible subsets of the factor’s arguments, i.e., O($2^n$) iterations for a factor with n arguments in the worst case. In this paper, we efficiently solve the problem of detecting commutative factors in a factor graph. In particular, we introduce the detection of commutative factors (DECOR) algorithm, which allows us to drastically reduce the computational effort for checking whether a factor is commutative in practice. We prove that DECOR efficiently identifies restrictions to drastically reduce the number of required iterations and validate the efficiency of DECOR in our empirical evaluation.},
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Gehrke, Marcel; Liebenow, Johannes; Mohammadi, Esfandiar; Braun, Tanya
Lifting in Support of Privacy-Preserving Probabilistic Inference Artikel
In: German Journal of Artificial Intelligence, 2024.
@article{Gehrke2024,
title = {Lifting in Support of Privacy-Preserving Probabilistic Inference},
author = {Marcel Gehrke and Johannes Liebenow and Esfandiar Mohammadi and Tanya Braun},
url = {https://link.springer.com/article/10.1007/s13218-024-00851-y},
year = {2024},
date = {2024-01-01},
journal = {German Journal of Artificial Intelligence},
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Luttermann, Malte; Machemer, Johann; Gehrke, Marcel
Efficient Detection of Exchangeable Factors in Factor Graphs Proceedings Article
In: Proceedings of the Thirty-Seventh International FLAIRS Conference (FLAIRS-2024), Florida Online Journals, 2024.
@inproceedings{Luttermann2024a,
title = {Efficient Detection of Exchangeable Factors in Factor Graphs},
author = {Malte Luttermann and Johann Machemer and Marcel Gehrke},
url = {https://journals.flvc.org/FLAIRS/article/view/135518},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the Thirty-Seventh International FLAIRS Conference (FLAIRS-2024)},
publisher = {Florida Online Journals},
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Luttermann, Malte; Hartwig, Mattis; Braun, Tanya; Möller, Ralf; Gehrke, Marcel
Lifted Causal Inference in Relational Domains Proceedings Article
In: Proceedings of the Third Conference on Causal Learning and Reasoning (CLeaR-2024), PMLR, 2024.
@inproceedings{Luttermann2024b,
title = {Lifted Causal Inference in Relational Domains},
author = {Malte Luttermann and Mattis Hartwig and Tanya Braun and Ralf Möller and Marcel Gehrke},
url = {https://proceedings.mlr.press/v236/luttermann24a.html},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the Third Conference on Causal Learning and Reasoning (CLeaR-2024)},
publisher = {PMLR},
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Luttermann, Malte; Braun, Tanya; Möller, Ralf; Gehrke, Marcel
Colour Passing Revisited: Lifted Model Construction with Commutative Factors Proceedings Article
In: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-2024), AAAI Press, 2024.
@inproceedings{Luttermann2024c,
title = {Colour Passing Revisited: Lifted Model Construction with Commutative Factors},
author = {Malte Luttermann and Tanya Braun and Ralf Möller and Marcel Gehrke},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/30034},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-2024)},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
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}
Anderson, Kathleen; Martinetz, Thomas
Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations Proceedings Article
In: International Conference on Artificial Neural Networks (ICANN) 2024, 2024.
@inproceedings{Anderson2024,
title = {Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations},
author = {Kathleen Anderson and Thomas Martinetz},
year = {2024},
date = {2024-01-01},
booktitle = {International Conference on Artificial Neural Networks (ICANN) 2024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Schulze, Max; Zisgen, Yorck; Kirschte, Moritz; Mohammadi, Esfandiar; Koschmider, Agnes
Differentially Private Inductive Miner Sonstige
2024.
@misc{schulze2024differentiallyprivateinductiveminer,
title = {Differentially Private Inductive Miner},
author = {Max Schulze and Yorck Zisgen and Moritz Kirschte and Esfandiar Mohammadi and Agnes Koschmider},
url = {https://arxiv.org/abs/2407.04595},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Kirschte, Moritz; Meiser, Sebastian; Ardalan, Saman; Mohammadi, Esfandiar
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers Sonstige
2024.
@misc{kirschte2024distributeddphelmetscalabledifferentially,
title = {Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers},
author = {Moritz Kirschte and Sebastian Meiser and Saman Ardalan and Esfandiar Mohammadi},
url = {https://arxiv.org/abs/2211.02003},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Luttermann, Malte; Möller, Ralf; Hartwig, Mattis
Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models Proceedings Article
In: Hotho, Andreas; Rudolph, Sebastian (Hrsg.): KI 2024: Advances in Artificial Intelligence, S. 175–189, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-70893-0.
@inproceedings{10.1007/978-3-031-70893-0_13,
title = {Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models},
author = {Malte Luttermann and Ralf Möller and Mattis Hartwig},
editor = {Andreas Hotho and Sebastian Rudolph},
isbn = {978-3-031-70893-0},
year = {2024},
date = {2024-01-01},
booktitle = {KI 2024: Advances in Artificial Intelligence},
pages = {175–189},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models, thereby allowing to represent relationships between objects in a relational domain. At the same time, the field of artificial intelligence requires increasingly large amounts of relational training data for various machine learning tasks. Collecting real-world data, however, is often challenging due to privacy concerns, data protection regulations, high costs, and so on. To mitigate these challenges, the generation of synthetic data is a promising approach. In this paper, we solve the problem of generating synthetic relational data via probabilistic relational models. In particular, we propose a fully-fledged pipeline to go from relational database to probabilistic relational model, which can then be used to sample new synthetic relational data points from its underlying probability distribution. As part of our proposed pipeline, we introduce a learning algorithm to construct a probabilistic relational model from a given relational database.},
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}
Liebenow, Johannes; Schütt, Yara; Braun, Tanya; Gehrke, Marcel; Thaeter, Florian; Mohammadi, Esfandiar
DPM: Clustering Sensitive Data through Separation Proceedings Article
In: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, S. 273–287, Association for Computing Machinery, Salt Lake City, UT, USA, 2024, ISBN: 9798400706363.
@inproceedings{10.1145/3658644.3690271,
title = {DPM: Clustering Sensitive Data through Separation},
author = {Johannes Liebenow and Yara Schütt and Tanya Braun and Marcel Gehrke and Florian Thaeter and Esfandiar Mohammadi},
url = {https://doi.org/10.1145/3658644.3690271},
doi = {10.1145/3658644.3690271},
isbn = {9798400706363},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security},
pages = {273–287},
publisher = {Association for Computing Machinery},
address = {Salt Lake City, UT, USA},
series = {CCS '24},
abstract = {Clustering is an important tool for data exploration where the goal is to subdivide a data set into disjoint clusters that fit well into the underlying data structure. When dealing with sensitive data, privacy-preserving algorithms aim to approximate the non-private baseline while minimising the leakage of sensitive information. State-of-the-art privacy-preserving clustering algorithms tend to output clusters that are good in terms of the standard metrics, inertia, silhouette score, and clustering accuracy, however, the clustering result strongly deviates from the non-private KMeans baseline. In this work, we present a privacy-preserving clustering algorithm called DPM that recursively separates a data set into clusters based on a geometrical clustering approach. In addition, DPM estimates most of the data-dependent hyper-parameters in a privacy-preserving way. We prove that DPM preserves Differential Privacy and analyse the utility guarantees of DPM. Finally, we conduct an extensive empirical evaluation for synthetic and real-life data sets. We show that DPM achieves state-of-the-art utility on the standard clustering metrics and yields a clustering result much closer to that of the popular non-private KMeans algorithm without requiring the number of classes.},
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pubstate = {published},
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}
Peinemann, Thorsten; Kirschte, Moritz; Stock, Joshua; Cotrini, Carlos; Mohammadi, Esfandiar
S-BDT: Distributed Differentially Private Boosted Decision Trees Proceedings Article
In: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, S. 288–302, Association for Computing Machinery, Salt Lake City, UT, USA, 2024, ISBN: 9798400706363.
@inproceedings{10.1145/3658644.3690301,
title = {S-BDT: Distributed Differentially Private Boosted Decision Trees},
author = {Thorsten Peinemann and Moritz Kirschte and Joshua Stock and Carlos Cotrini and Esfandiar Mohammadi},
url = {https://doi.org/10.1145/3658644.3690301},
doi = {10.1145/3658644.3690301},
isbn = {9798400706363},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security},
pages = {288–302},
publisher = {Association for Computing Machinery},
address = {Salt Lake City, UT, USA},
series = {CCS '24},
abstract = {We introduce S-BDT: a novel (ε,δ)-differentially private distributed gradient boosted decision tree (GBDT) learner that improves the protection of single training data points (privacy) while achieving meaningful learning goals, such as accuracy or regression error (utility). S-BDT uses less noise by relying on non-spherical multivariate Gaussian noise, for which we show tight subsampling bounds for privacy amplification and incorporate that into a Rényi filter for individual privacy accounting. We experimentally reach the same utility while saving 50% in terms of epsilon for ε ≤ 0.5 on the Abalone regression dataset (dataset size ≈ 4K), saving 30% in terms of epsilon for ε ≤ 0.08 for the Adult classification dataset (dataset size ≈ 50K), and saving 30% in terms of epsilon for ε ≤ 0.03 for the Spambase classification dataset (dataset size ≈ 5K). Moreover, we show that for situations where a GBDT is learning a stream of data that originates from different subpopulations (non-IID), S-BDT improves the saving of epsilon even further.},
keywords = {},
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Luttermann, Malte; Möller, Ralf; Gehrke, Marcel
Lifting Factor Graphs with Some Unknown Factors Proceedings Article
In: Proceedings of the Seventeenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-2023), Springer, 2023.
@inproceedings{Luttermann2023,
title = {Lifting Factor Graphs with Some Unknown Factors},
author = {Malte Luttermann and Ralf Möller and Marcel Gehrke},
url = {https://link.springer.com/chapter/10.1007/978-3-031-45608-4_25},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the Seventeenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-2023)},
publisher = {Springer},
keywords = {},
pubstate = {published},
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Brügge, Nele Sophie; Mohammadi, Esfandiar; Münchau, Alexander; Bäumer, Tobias; Frings, Christian; Beste, Christian; Roessner, Veit; Handels, Heinz
Towards Privacy and Utility in Tourette Tic Detection Through Pretraining Based on Publicly Available Video Data of Healthy Subjects Proceedings Article
In: ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
@inproceedings{10095309,
title = {Towards Privacy and Utility in Tourette Tic Detection Through Pretraining Based on Publicly Available Video Data of Healthy Subjects},
author = {Nele Sophie Brügge and Esfandiar Mohammadi and Alexander Münchau and Tobias Bäumer and Christian Frings and Christian Beste and Veit Roessner and Heinz Handels},
doi = {10.1109/ICASSP49357.2023.10095309},
year = {2023},
date = {2023-01-01},
booktitle = {ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Sander, Jonas; Berndt, Sebastian; Bruhns, Ida; Eisenbarth, Thomas
DASH: Accelerating Distributed Private Machine Learning Inference with Arithmetic Garbled Circuits Artikel
In: CoRR, Bd. abs/2302.06361, 2023.
@article{DBLP:journals/corr/abs-2302-06361,
title = {DASH: Accelerating Distributed Private Machine Learning Inference
with Arithmetic Garbled Circuits},
author = {Jonas Sander and Sebastian Berndt and Ida Bruhns and Thomas Eisenbarth},
url = {https://doi.org/10.48550/arXiv.2302.06361},
doi = {10.48550/ARXIV.2302.06361},
year = {2023},
date = {2023-01-01},
journal = {CoRR},
volume = {abs/2302.06361},
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pubstate = {published},
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