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Friedrich-Alexander-Universität Erlangen-Nürnberg

Academic institutioneurope · de
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Research library553linked papers
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Selected work

Representative Papers

Defining Atomicity (and Integrity) for Snapshots of Storage in Forensic Computing

May 21, 2025

In digital forensics, the atomicity and integrity of storage snapshots lack rigorous definitions that jointly guarantee both instantaneousness and causal ordering—undermining evidentiary admissibility in legal proceedings. To address this, we propose a novel atomicity definition grounded in causal consistency, overcoming the limitation of conventional time-based atomicity models. We further rectify conceptual flaws in existing integrity definitions and introduce a revised, theoretically sound yet engineering-practical integrity criterion—explicitly supporting copy-on-write (CoW) implementations. Our approach integrates causal modeling, formal snapshot semantics, CoW mechanism analysis, and formalization of forensic quality criteria, yielding a verifiable snapshot semantic framework. This work establishes the first theoretical foundation for forensic tool design that unifies causal ordering with instantaneous state capture, thereby significantly enhancing the forensic validity and judicial admissibility of live data acquisition.

7 citations4 influentialRead paper

In Search of Lost Data: A Study of Flash Sanitization Practices

May 20, 2025

This study addresses the widespread failure of thorough data sanitization in “brand-new” USB flash drives sold at low prices in the Chinese market. We conducted the first large-scale empirical digital forensic investigation, systematically examining 614 manufacturer-labeled USB drives using digital forensic analysis, physical-layer NAND flash data recovery, batch automated detection, and metadata correlation analysis. Results revealed that 12.2% of devices retained recoverable non-trivial user data—including 75 instances containing sensitive or illicit files—with no statistically significant external predictors of data remanence. This work is the first to empirically demonstrate systemic sanitization failure in chip-reuse scenarios, directly challenging the default assumption of consumer-grade storage devices being “trusted out-of-the-box.” Our findings provide critical empirical evidence for data security governance, supply-chain risk assessment, and the development of judicial digital forensic standards.

6 citations1 influentialRead paper

Cut to the Mix: Simple Data Augmentation Outperforms Elaborate Ones in Limited Organ Segmentation Datasets

Feb 03, 2026International Conference on Medical Image Computing and Computer-Assisted Intervention

This study addresses the challenge of limited clinical annotations in multi-organ segmentation by systematically evaluating four cross-image data augmentation strategies—CutMix, CarveMix, ObjectAug, and AnatoMix—integrated with conventional augmentation techniques within the nnUNet framework. Despite its semantically implausible image composition, CutMix demonstrates consistently strong and robust performance improvements, outperforming more sophisticated augmentation methods. Experimental results show that CutMix, CarveMix, and AnatoMix increase the average Dice score by 4.9%, 2.0%, and 1.9%, respectively, with further gains observed when combined with traditional augmentations. These findings underscore the efficacy and practicality of simple cross-image augmentation approaches in medical image segmentation tasks.

5 citationsRead paper

Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation

May 17, 2024Radiotherapy and Oncology

This study addresses the challenge of automatic multi-center brain metastasis segmentation while overcoming medical data silos and ensuring privacy compliance. We propose the first framework that deeply integrates differential privacy with federated learning, featuring a dual privacy-preserving mechanism: gradient clipping combined with noise injection during local training, and secure model aggregation across sites—enabling collaborative training without sharing raw medical images. Leveraging a 3D U-Net architecture and a heterogeneous data alignment strategy, our method achieves a Dice score of 0.892 ± 0.021 on data from six hospitals—representing a 12.3% improvement over single-center baselines. Under a privacy budget of ε = 2.0, the framework rigorously satisfies HIPAA and GDPR requirements. This work establishes a verifiable, deployable paradigm for privacy-sensitive, cross-institutional AI modeling in clinical neuro-oncology.

3 citationsRead paper

Directional Sparsity Based Statistical Channel Estimation for 6D Movable Antenna Communications

May 21, 2025

For six-degree-of-freedom movable antennas (6DMA), acquiring accurate statistical channel state information (CSI) incurs prohibitively high overhead and suffers from low estimation accuracy. This work first reveals a pronounced directional sparsity of the 6DMA channel in the joint position–orientation domain. Leveraging this insight, we propose a covariance-driven statistical CSI estimation framework that reconstructs the full-region average channel power distribution with high fidelity using only a small number of position–orientation samples. Our method jointly estimates multipath average power and directions of arrival (DOAs), circumventing explicit geometric propagation modeling. Experimental results demonstrate that, compared to baseline schemes, the proposed approach reduces pilot overhead by over 80% and decreases the mean squared error of channel power estimation by up to 45%, validating both the effectiveness of directional sparsity modeling and its practical feasibility for 6DMA systems.

2 citationsRead paper
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