How Did Dread Pirate Roberts Acquire and Protect his Bitcoin Wealth?

Abstract. The Bitcoin scheme is one of the most popular and talked about alternative payment schemes. One of the most active parts of the Bitcoin ecosystem was the Silk Road marketplace, in which highly illegal substances and services were traded. It was run by a person who called himself Dread Pirate Roberts (DPR), whose bitcoin holdings are esti-mated to be worth hundreds of millions of dollars at today’s exchange rate. On October 1-st 2013, the FBI arrested a 29 year old person named Ross William Ulbricht, claiming that he is DPR, and seizing a small fraction of his bitcoin wealth. In this paper we use the publicly available record to trace the evolution of his holdings in order to find how he ac-quired and how he tried to hide them from the authorities. In particular, we trace the amounts he received and the amounts he transferred out of his accounts, and show that all his Silk Road commissions from the months of May, June and September 2013, along with numerous other amounts, were not seized by the FBI. This analysis demonstrates the power of data mining techniques in analyzing large payment systems, and especially publicly available transaction graphs of the type provided by the Bitcoin scheme

Lead in Archeological Human Bones Reflecting Historical Changes in Lead Production.

Forty years ago, in a seminal paper published in Science, Settle and Patterson used archeological and historical data to estimate the rates of worldwide lead production since the discovery of cupellation, approximately 5000 years ago. Here, we record actual lead exposure of a human population by direct measurements of the concentrations of lead in petrous bones of individuals representing approximately 12 000 years of inhabitation in Italy. This documentation of lead pollution throughout human history indicates that, remarkably, much of the estimated dynamics in lead production is replicated in human exposure. Thus, lead pollution in humans has closely followed anthropogenic lead production. This observation raises concerns that the forecasted increase in the production of lead and other metals might affect human health in the near future

EXPANDER – an integrative program suite for microarray data analysis

BACKGROUND: Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. RESULTS: Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. CONCLUSION: EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users a

Privacy-Preserving Decision Tree Training and Prediction against Malicious Server

Privacy-preserving machine learning enables secure outsourcing of machine learning tasks to an untrusted service provider (server) while preserving the privacy of the user\u27s data (client). Attaining good concrete efficiency for complicated machine learning tasks, such as training decision trees, is one of the challenges in this area. Prior works on privacy-preserving decision trees required the parties to have comparable computational resources, and instructed the client to perform computation proportional to the complexity of the entire task. In this work we present new protocols for privacy-preserving decision trees, for both training and prediction, achieving the following desirable properties: 1. Efficiency: the client\u27s complexity is independent of the training-set size during training, and of the tree size during prediction. 2. Security: privacy holds against malicious servers. 3. Practical usability: high accuracy, fast prediction, and feasible training demonstrated on standard UCI datasets, encrypted with fully homomorphic encryption. To the best of our knowledge, our protocols are the first to offer all these properties simultaneously. The core of our work consists of two technical contributions. First, a new low-degree polynomial approximation for functions, leading to faster protocols for training and prediction on encrypted data. Second, a design of an easy-to-use mechanism for proving privacy against malicious adversaries that is suitable for a wide family of protocols, and in particular, our protocols; this mechanism could be of independent interest