Data mining for detecting Bitcoin Ponzi schemes

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives

Blockchain for social good: a quantitative analysis

The rise of blockchain technologies has given a boost to social good projects, which are trying to exploit various characteristic features of blockchains: the quick and inexpensive transfer of cryptocurrency, the transparency of transactions, the ability to tokenize any kind of assets, and the increase in trustworthiness due to decentralization. However, the swift pace of innovation in blockchain technologies, and the hype that has surrounded their "disruptive potential", make it difficult to understand whether these technologies are applied correctly, and what one should expect when trying to apply them to social good projects. This paper addresses these issues, by systematically analysing a collection of 120 blockchain-enabled social good projects. Focussing on measurable and objective aspects, we try to answer various relevant questions: which features of blockchains are most commonly used? Do projects have success in fund raising? Are they making appropriate choices on the blockchain architecture? How many projects are released to the public, and how many are eventually abandoned?Comment: In GOODTECHS 201

Safety and efficacy of direct-acting antivirals in transfusion-dependent thalassemic patients with chronic hepatitis C

Background: Hepatitis C virus (HCV) infection is a major cause of liver-related morbidity and mortality among thalassemic patients. New treatments based on direct-acting antivirals (DAAs) are highly effective and well-tolerated by patients; nonetheless, they have not been studied in thalassemic populations. In this study, we evaluated the safety and efficacy of these treatments in a cohort of Sardinian thalassemic patients with chronic HCV infection. Methods: We consecutively recruited thalassemic patients with HCV infection, who were eligible for DAA therapy at 3 liver units. Different drug combinations, depending on HCV genotype and hepatic disease severity, were used according to the current guidelines. Sustained virological response was assessed at 12 weeks posttreatment. Data regarding the side effects and transfusion requirements were also collected. Results: We recruited 49 patients, including 29 males (59.2%), with the mean age of 43 years (genotype 1, 55.1%). Twenty-one (42.9%) patients had a history of interferon-based treatment. Cirrhosis was detected in 28 (57.1%) patients; only 1 patient had ascites and hypoalbuminemia (Child-Pugh B7). On the other hand, 35 (71.4%) patients received a sofosbuvir-based regimen. Ribavirin treatment was reported in 26 (53.1%) cases. All the patients were followed-up for at least 12 weeks after therapy, and sustained virological response was observed in 98% of the patients. No treatment discontinuation was required due to adverse events. The most common side effects included fatigue (24.5%), headache (10.2%), and anaemia (77%), requiring further blood transfusion in patients receiving ribavirin. Conclusions: This prospective study showed that DAAs are safe and effective agents in thalassemic patients with advanced liver fibrosis, regardless of previous antiviral treatment responses

Data mining for detecting Bitcoin Ponzi schemes

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives