Search Results for author:
Found 6 papers, 2 papers with code
S-GBDT: Frugal Differentially Private Gradient Boosting Decision Trees
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.
DPM: Clustering Sensitive Data through Separation
Privacy-preserving clustering groups data points in an unsupervised manner whilst ensuring that sensitive information remains protected.
Single SMPC Invocation DPHelmet: Differentially Private Distributed Learning on a Large Scale
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.
Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms
An additive mechanism with truncated noise (i. e., with bounded range) can offer such hard bounds.
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions
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.
Computational Soundness for Dalvik Bytecode
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
