Observational cohort studies are a powerful tool to assess the long-term outcome in chronic diseases. This study design has been utilized in local and regional outcome studies in multiple sclerosis (MS) and has yielded invaluable epidemiological information. The World Wide Web now provides an excellent opportunity for an international, collaborative cohort study of MS outcomes. A web platform--MSBase--has been designed to collect prospective data on patients with MS. It is purely observational, enabling participating neurologists to contribute data on diagnosis, treatment and progress, to review anonymous aggregate data and to benchmark their patient population against other patient subsets or the entire dataset. MSBase facilitates collaborative research by allowing the online creation of investigator-initiated regional, national and international substudies. The registry aims to answer epidemiological questions that can only be addressed by prospective assessments of large patient cohorts. The registry is funded through the independent MSBase Foundation, and governed by an International Scientific Advisory Board. The MSBase Foundation commenced operations in July 2004 and since then, 22 neurologists from 11 countries have joined MSBase and are contributing 2400 patients to the total data pool.
This work revisits zero-knowledge proofs in the discrete logarithm setting. First, we identify and carve out basic techniques (partly being used implicitly before) to optimise proofs in this setting. In particular, the linear combination of protocols is a useful tool to obtain zero-knowledge and/or reduce communication. With these techniques, we are able to devise zero-knowledge variants of the logarithmic communication arguments by Bootle et al. (EUROCRYPT '16) and Bünz et al. (S&P '18) thereby introducing almost no overhead. We then construct a conceptually simple commitand-prove argument for satisfiability of a set of quadratic equations. Unlike previous work, we are not restricted to rank 1 constraint systems (R1CS). This is, to the best of our knowledge, the first work demonstrating that general quadratic constraints, not just R1CS, are a natural relation in the dlog (or ideal linear commitment) setting. This enables new possibilities for optimisation, as, e.g., any degree n 2 polynomial f (X) can now be "evaluated" with at most 2n quadratic constraints.Additionally, we take a closer look at quantitative measures, e.g. the efficiency of an extractor. For this, we formalise short-circuit extraction, which allows us to give tighter bounds on the efficiency of an extractor.
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