Add to ORKGPrivacy-preserving machine learning enables the training of models on decentralized datasets without the need to reveal the data, both on horizontal and vertically partitioned data. However, it relies on specialized techniques and algorithms to perform the necessary computations. The privacy preserving scalar product protocol, which enables the dot product of vectors without revealing them, is one popular example for its versatility. Unfortunately, the solutions currently proposed in the literature focus mainly on two-party scenarios, even though scenarios with a higher number of data parties are becoming more relevant. For example when performing analyses that require counting the number of samples which fulfill certain criteria defined ac...
Machine learning algorithms, such as neural networks, create better predictive models when having ac...
Cross-organizational collaborative decision-making involves a great deal of private information whic...
This research explores ways to effectively use distributed machine learning while preserving privac...
Privacy-preserving machine learning enables the training of models on decentralized datasets without...
Abstract. In mining and integrating data from multiple sources, there are many privacy and security ...
The recent investigation of privacy-preserving data mining has been motivated by the growing concern...
We propose a novel two-party privacy-preserving classification solution called Collaborative Classif...
The past decade has witnessed the fast growth and tremendous success of machine learning. However, r...
We propose privacy-preserving protocols for computing linear regression models, in the setting where...
Abstract. The secure scalar product (or dot product) is one of the most used sub-protocols in privac...
Data mining is a process to extract useful knowledge from large amounts of data. To conduct data min...
We show how multiple data-owning parties can collaboratively train several machine learning algorith...
In this paper, we address the problem of privacy-preserving distributed learning and the evaluation ...
We consider the problem of maintaining sparsity in private distributed storage of confidential machi...
We consider training machine learning models using data located on multiple private and geographical...
Machine learning algorithms, such as neural networks, create better predictive models when having ac...
Cross-organizational collaborative decision-making involves a great deal of private information whic...
This research explores ways to effectively use distributed machine learning while preserving privac...
Privacy-preserving machine learning enables the training of models on decentralized datasets without...
Abstract. In mining and integrating data from multiple sources, there are many privacy and security ...
The recent investigation of privacy-preserving data mining has been motivated by the growing concern...
We propose a novel two-party privacy-preserving classification solution called Collaborative Classif...
The past decade has witnessed the fast growth and tremendous success of machine learning. However, r...
We propose privacy-preserving protocols for computing linear regression models, in the setting where...
Abstract. The secure scalar product (or dot product) is one of the most used sub-protocols in privac...
Data mining is a process to extract useful knowledge from large amounts of data. To conduct data min...
We show how multiple data-owning parties can collaboratively train several machine learning algorith...
In this paper, we address the problem of privacy-preserving distributed learning and the evaluation ...
We consider the problem of maintaining sparsity in private distributed storage of confidential machi...
We consider training machine learning models using data located on multiple private and geographical...
Machine learning algorithms, such as neural networks, create better predictive models when having ac...
Cross-organizational collaborative decision-making involves a great deal of private information whic...
This research explores ways to effectively use distributed machine learning while preserving privac...