Cyber Security National Lab - UniPg

Analyzing in deep the block-chain in the Bitcoin network

winner of the AWS Research Grant

The aim behind our research is to analyze in deep the block-chain in the Bitcoin network. By analyzing the block-chain and correlating it with this publicly available meta data, we aim to find out how much an address is used for e.g., mixing activities (e.g., money laundering), if it was used for scamming users in the past, if and how it is related to other addresses and entities. We will use heuristics to group addresses in clusters that correspond with entities that control them. Collapsing addresses into clusters compacts and simplifies the huge transaction graph, creating edges between users that correspond to aggregate transactions.

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Data Science for investigating the block-chain in Bitcoin

winner of the Microsoft Azure Award

The aim behind our research is to analyze in deep the block-chain in the Bitcoin network. To accomplish this, we will exploit the cloud computing resources offered by Microsoft. By analyzing the block-chain and correlating it with public available meta data, it is possible to find out whether a given address is used for e.g., mixing activities (e.g., for money laundering), if it was used for scamming users in the past, and, in general, if and how it is related to other addresses and entities. Addresses can be algorithmically grouped into clusters that are related with the entities that control them. Collapsing addresses into clusters compacts and simplifies such a huge transaction graph.

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Firma Digitale

Progetto in collaborazione con l'università di Bari, diviso in Analisi e Sviluppo di un Classificatore, necessario ad autenticare una firma autografa utilizzando esclusivamente immagini delle firme già accreditate. La fase di Analisi consiste nello studio approfondito degli algoritmi e delle strutture esistenti per la costruzione del Classificatore. La seconda fase, prevede la realizzazione di un algoritmo, derivato dallo studio precedente, e la codifica di un Prototipo del Classificatore, in linguaggio Java.

Bitcoin

Il progetto Bitcoin si propone di studiare l’asset valuta elettronica Bitcoin. In particolare, sarà studiato e definito un modello matematico per la descrizione della dinamica del prezzo del Bitcoin, prima in un solo mercato e poi analizzando i meccanismi d’interazione tra mercati diversi. Sulla base di tale studio sarà individuato un modello discreto continuo adeguato che, da una analisi preliminare, sembrerebbe suggerire la presenza di bolle speculative delle quali si dovrà tenere conto nella scelta delle dinamiche di prezzo.

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AMANDA: Algorithms for MAssive and Networked DAta

The Engineering Department of the University of Perugia is one of the units involved in the PRIN 2012 project "AMANDA: Algorithms for MAssive and Networked DAta". The unit is coordinated by Prof. Giuseppe Liotta, and includes the researchers of the Computer Engineering group of the Department, namely Prof. Walter Didimo, Dr. Emilio Di Giacomo, Dr. Carla Binucci, Dr. Luca Grilli, and Dr. Fabrizio Montecchiani.

AMANDA will investigate algorithmics for massive data sets. On one hand the project will study emerging and realistic computational models and general algorithm design techniques; on the other hand it will focus on algorithmic issues specific for networked data sets. Pursuing these objectives raises hard research challenges, since the size of the data as well as their networked and evolving nature require a quantum leap in algorithmic design and engineering. These challenges are addressed in two workparts (WPs), each combining theoretical analysis with extensive experimental validation:

Other than the University of Perugia, the AMANDA consortium includes the Third University of Rome (general coordinator), the University of Rome "La Sapienza", the University of Rome "Tor Vergata", the University of Pisa, and the University of Padova. The goal of AMANDA is to strengthen the world leading position of Italian algorithmic research and the European excellence in science in general. Some of AMANDA's expected results are likely to be exploited by industries, thus providing them support in the big data challenge, while others have a foreseeable social impact.