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 • 6 Jul 2023 • Yara Schütt, Johannes Liebenow, Tanya Braun, Marcel Gehrke, Florian Thaeter, Esfandiar Mohammadi
Privacy-preserving clustering groups data points in an unsupervised manner whilst ensuring that sensitive information remains protected.
1 code implementation • 3 Nov 2022 • Moritz Kirschte, Sebastian Meiser, Saman Ardalan, Esfandiar Mohammadi
We show that locally training support vector machines (SVMs) and computing their averages leads to a learning technique that is scalable to a large number of users, satisfies differential privacy, and is applicable to non-trivial tasks, such as CIFAR-10.
1 code implementation • 27 Jul 2021 • David M. Sommer, Lukas Abfalterer, Sheila Zingg, Esfandiar Mohammadi
An additive mechanism with truncated noise (i. e., with bounded range) can offer such hard bounds.
no code implementations • 20 Jul 2021 • Eike Petersen, Yannik Potdevin, Esfandiar Mohammadi, Stephan Zidowitz, Sabrina Breyer, Dirk Nowotka, Sandra Henn, Ludwig Pechmann, Martin Leucker, Philipp Rostalski, Christian Herzog
This survey provides an overview of the technical and procedural challenges involved in creating medical machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address these challenges.
no code implementations • 15 Aug 2016 • Michael Backes, Robert Künnemann, Esfandiar Mohammadi
Second, we show that our abstractions are faithful by providing the first computational soundness result for Dalvik bytecode, i. e., the absence of attacks against our symbolically abstracted program entails the absence of any attacks against a suitable cryptographic program realization.
Cryptography and Security