no code implementations • 21 Sep 2023 • Moritz Kirschte, Thorsten Peinemann, Joshua Stock, Carlos Cotrini, Esfandiar Mohammadi
For the Abalone dataset for $\varepsilon=0. 54$ we achieve $R^2$-score of $0. 47$ which is very close to the $R^2$-score of $0. 54$ for the nonprivate version of GBDT.
no code implementations • 28 Jul 2023 • Joshua Stock, Oliver Hauke, Julius Weißmann, Hannes Federrath
This work investigates the potential of Federated Learning (FL) for official statistics and shows how well the performance of FL models can keep up with centralized learning methods. F L is particularly interesting for official statistics because its utilization can safeguard the privacy of data holders, thus facilitating access to a broader range of data.
no code implementations • 18 May 2022 • Joshua Stock, Jens Wettlaufer, Daniel Demmler, Hannes Federrath
Extensive experiments with property unlearning show that while it is very effective when defending target models against specific adversaries, property unlearning is not able to generalize, i. e., protect against a whole class of PIAs.
no code implementations • 10 Aug 2020 • Rosario Cammarota, Matthias Schunter, Anand Rajan, Fabian Boemer, Ágnes Kiss, Amos Treiber, Christian Weinert, Thomas Schneider, Emmanuel Stapf, Ahmad-Reza Sadeghi, Daniel Demmler, Joshua Stock, Huili Chen, Siam Umar Hussain, Sadegh Riazi, Farinaz Koushanfar, Saransh Gupta, Tajan Simunic Rosing, Kamalika Chaudhuri, Hamid Nejatollahi, Nikil Dutt, Mohsen Imani, Kim Laine, Anuj Dubey, Aydin Aysu, Fateme Sadat Hosseini, Chengmo Yang, Eric Wallace, Pamela Norton
Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time.