Publikationen

@inproceedings{10.1007/978-3-031-76235-2_20,

title = {Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs},

author = {Malte Luttermann and Tanya Braun and Ralf Möller and Marcel Gehrke},

editor = {Sébastien Destercke and Maria Vanina Martinez and Giuseppe Sanfilippo},

isbn = {978-3-031-76235-2},

year  = {2025},

date = {2025-01-01},

booktitle = {Scalable Uncertainty Management},

pages = {265–280},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference while maintaining exact answers. In this paper, we show how lifting can be applied to causal inference in partially directed graphs, i.e., graphs that contain both directed and undirected edges to represent causal relationships between directed and undirected edges to represent causal relationships between random variables. We present partially directed parametric causal factor graphs (PPCFGs) as a generalisation of previously introduced parametric causal factor graphs, which require a fully directed graph. We further show how causal inference can be performed on a lifted level in PPCFGs, thereby extending the applicability of lifted causal inference to a broader range of models requiring less prior knowledge about causal relationships.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bruegge2025,

title = {Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data},

author = {Nele Sophie Brügge and Alexandra Korda and Stefan Borgwardt and Christina Andreou and Giorgos Giannakakis and Heinz Handels},

url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0013364900003911},

year  = {2025},

date = {2025-01-01},

booktitle = {Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF, 2025},

pages = {285-296},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10680684,

title = {Differentially Private Inductive Miner},

author = {Max Schulze and Yorck Zisgen and Moritz Kirschte and Esfandiar Mohammadi and Agnes Koschmider},

doi = {10.1109/ICPM63005.2024.10680684},

year  = {2024},

date = {2024-10-01},

booktitle = {2024 6th International Conference on Process Mining (ICPM)},

pages = {89-96},

abstract = {Protecting personal data about individuals, such as event traces in process mining, is an inherently difficult task since an event trace leaks information about the path in a process model that an individual has triggered. Yet, prior anonymization methods of event traces like k-anonymity or event log sanitization struggled to protect against such leakage, in particular against adversaries with sufficient background knowledge. In this work, we provide a method that tackles the challenge of summarizing sensitive event traces by learning the underlying process tree in a privacy-preserving manner. We prove via the so-called Differential Privacy (DP) property that from the resulting summaries no useful inference can be drawn about any personal data in an event trace. On the technical side, we introduce a differentially private approximation (DPIM) of the Inductive Miner. Experimentally, we compare our DPIM with the Inductive Miner on 14 real-world event traces by evaluating well-known metrics: fitness, precision, simplicity, and generalization. The experiments show that our DPIM not only protects personal data but also generates faithful process trees that exhibit little utility loss above the Inductive Miner.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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},

tppubtype = {inproceedings}

}

@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}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wilke2024snpguard,

title = {SNPGuard: Remote Attestation of SEV-SNP VMs Using Open Source Tools},

author = {Luca Wilke and Gianluca Scopelliti},

url = {https://ieeexplore.ieee.org/document/10628964/},

doi = {10.1109/EuroSPW61312.2024.00026},

year  = {2024},

date = {2024-01-01},

booktitle = {2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)},

pages = {193–198},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wilke2024tdxdown,

title = {TDXdown: Single-Stepping and Instruction Counting Attacks against Intel TDX},

author = {Luca Wilke and Florian Sieck and Thomas Eisenbarth},

url = {https://dl.acm.org/doi/10.1145/3658644.3690230},

doi = {10.1145/3658644.3690230},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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},

tppubtype = {inproceedings}

}

@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}

}