We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes ...
Data mining algorithms use various Trie and bitmap-based representations to optimize the support (i.e., frequency) counting performance. In this paper, we compare the memory requi...
In this paper we explore private computation built on vector addition and its applications in privacypreserving data mining. Vector addition is a surprisingly general tool for imp...
Web-based learning environments are extensively used nowadays. These environments maintain and produce vast amounts of data. Such vastness lead to the application of data mining t...
Ioannis Kazanidis, Stavros Valsamidis, Theodosios ...