Thomas Grübl

Chao Feng, Thomas Grubl, Jan von der Assen et al.

Decentralized Federated Learning (DFL) removes the central aggregator of conventional Federated Learning, but peer-to-peer model exchange still exposes network traces: who communicates, when fragments move, and which packets correlate across rounds. This paper studies network-layer sender--message linkability for DFL model sharing and presents UnlinkableDFL, a framework in which every participant acts as both a learner and a peer-based mix relay. Shareable model states are split into uniform, onion-encrypted fragment packets and carried over a peer-run mixnet with cover traffic, randomized delays, and independently sampled multi-hop paths. Nodes then perform fragmented aggregation over local and received fragments without sender identities. The analysis bounds sender-linking probability through route uncertainty and relay shuffles, and characterizes when fragment-level aggregation preserves FedAvg-style behavior. A prototype implements QUIC transport, Sphinx-style packets, and Single-Use Reply Block (SURB) acknowledgments. Experiments show that the design sustains learning under sparse deployment while exposing a privacy--cost trade-off: path diversity and relay mixing raise network-layer uncertainty, whereas delay and forwarding dominate overhead. Stress tests confirm robustness to churn and Byzantine updates. A curious-recipient attack marks the boundary of the network-layer guarantee, where payload-level fingerprints survive network-layer anonymization and need complementary defenses, although partial updates and more IID data weaken this attack surface.

Thomas Grubl, Harjinder Singh Lallie

The precise age estimation of child sexual abuse and exploitation (CSAE) victims is one of the most significant digital forensic challenges. Investigators often need to determine the age of victims by looking at images and interpreting the sexual development stages and other human characteristics. The main priority - safeguarding children -- is often negatively impacted by a huge forensic backlog, cognitive bias and the immense psychological stress that this work can entail. This paper evaluates existing facial image datasets and proposes a new dataset tailored to the needs of similar digital forensic research contributions. This small, diverse dataset of 0 to 20-year-old individuals contains 245 images and is merged with 82 unique images from the FG-NET dataset, thus achieving a total of 327 images with high image diversity and low age range density. The new dataset is tested on the Deep EXpectation (DEX) algorithm pre-trained on the IMDB-WIKI dataset. The overall results for young adolescents aged 10 to 15 and older adolescents/adults aged 16 to 20 are very encouraging -- achieving MAEs as low as 1.79, but also suggest that the accuracy for children aged 0 to 10 needs further work. In order to determine the efficacy of the prototype, valuable input of four digital forensic experts, including two forensic investigators, has been taken into account to improve age estimation results. Further research is required to extend datasets both concerning image density and the equal distribution of factors such as gender and racial diversity.