SENov 21, 2019Code
Kooplex: collaborative data analytics portal for advancing sciencesDávid Visontai, József Stéger, János Márk Szalai-Gindl et al.
Research collaborations are continuously emerging catalyzed by online platforms, where people can share their codes, calculations, data and results. These virtual research platforms are innovative, community oriented, flexible and secure as required by modern scientific approaches. A wide range of open source and commercial solutions are available in this field emphasizing the relevant aspects of such a platform differently. In this paper we present our open source and modular platform, KOOPLEX, which combines the key concepts of dynamic collaboration, customizable research environment, data sharing, access to datahubs, reproducible research and reporting. It is easily deployable and scalable to serve more users or access large computational resources.
APDec 20, 2016
A Bayesian Approach to Identify Bitcoin UsersPéter L. Juhász, József Stéger, Dániel Kondor et al.
Bitcoin is a digital currency and electronic payment system operating over a peer-to-peer network on the Internet. One of its most important properties is the high level of anonymity it provides for its users. The users are identified by their Bitcoin addresses, which are random strings in the public records of transactions, the blockchain. When a user initiates a Bitcoin-transaction, his Bitcoin client program relays messages to other clients through the Bitcoin network. Monitoring the propagation of these messages and analyzing them carefully reveal hidden relations. In this paper, we develop a mathematical model using a probabilistic approach to link Bitcoin addresses and transactions to the originator IP address. To utilize our model, we carried out experiments by installing more than a hundred modified Bitcoin clients distributed in the network to observe as many messages as possible. During a two month observation period we were able to identify several thousand Bitcoin clients and bind their transactions to geographical locations.