Property Unlearning: A Defense Strategy Against Property Inference Attacks. - Joshua Stock; Jens Wettlaufer; Daniel Demmler; Hannes Federrath (CoRR, 2022)

Privatsphärefreundliches maschinelles Lernen. - Joshua Stock; Tom Petersen; Christian-Alexander Behrendt; Hannes Federrath; Thea Kreutzburg (Inform. Spektrum, 2022)

LoVe is in the Air - Location Verification of ADS-B Signals using Distributed Public Sensors. - Johanna Ansohn McDougall; Alessandro Brighente; Willi Großmann; Ben Ansohn McDougall; Joshua Stock; Hannes Federrath (ICC, 2023)

The Applicability of Federated Learning to Official Statistics. - Joshua Stock; Oliver Hauke; Julius Weißmann; Hannes Federrath (IDEAL, 2023)

Lessons Learned: Defending Against Property Inference Attacks. - Joshua Stock; Jens Wettlaufer; Daniel Demmler; Hannes Federrath (SECRYPT, 2023)

S-GBDT: Frugal Differentially Private Gradient Boosting Decision Trees. - Moritz Kirschte; Thorsten Peinemann; Joshua Stock; Carlos Cotrini; Esfandiar Mohammadi (CoRR, 2023)

DealSecAgg: Efficient Dealer-Assisted Secure Aggregation for Federated Learning. - Joshua Stock; Henry Heitmann; Janik Noel Schug; Daniel Demmler (ARES, 2024)

S-BDT: Distributed Differentially Private Boosted Decision Trees. - Thorsten Peinemann; Moritz Kirschte; Joshua Stock; Carlos Cotrini; Esfandiar Mohammadi (CCS, 2024)

Property Inference as a Regression Problem: Attacks and Defense. - Joshua Stock; Lucas Lange; Erhard Rahm; Hannes Federrath (SECRYPT, 2024)