In 1998, Blaze, Bleumer, and

Abstract-Bitcoin is the first digital currency to see widespread adoption. While payments are conducted between pseudonyms, Bitcoin cannot offer strong privacy guarantees: payment transactions are recorded in a public decentralized ledger, from which much information can be deduced. Zerocoin (Miers et al., IEEE S&P 2013) tackles some of these privacy issues by unlinking transactions from the payment's origin. Yet, it still reveals payments' destinations and amounts, and is limited in functionality.In this paper, we construct a full-fledged ledger-based digital currency with strong privacy guarantees. Our results leverage recent advances in zero-knowledge Succinct Non-interactive ARguments of Knowledge (zk-SNARKs).First, we formulate and construct decentralized anonymous payment schemes (DAP schemes). A DAP scheme enables users to directly pay each other privately: the corresponding transaction hides the payment's origin, destination, and transferred amount. We provide formal definitions and proofs of the construction's security.Second, we build Zerocash, a practical instantiation of our DAP scheme construction. In Zerocash, transactions are less than 1 kB and take under 6 ms to verify -orders of magnitude more efficient than the less-anonymous Zerocoin and competitive with plain Bitcoin.

Expression Atlas is EMBL-EBI’s resource for gene and protein expression. It sources and compiles data on the abundance and localisation of RNA and proteins in various biological systems and contexts and provides open access to this data for the research community. With the increased availability of single cell RNA-Seq datasets in the public archives, we have now extended Expression Atlas with a new added-value service to display gene expression in single cells. Single Cell Expression Atlas was launched in 2018 and currently includes 123 single cell RNA-Seq studies from 12 species. The website can be searched by genes within or across species to reveal experiments, tissues and cell types where this gene is expressed or under which conditions it is a marker gene. Within each study, cells can be visualized using a pre-calculated t-SNE plot and can be coloured by different features or by cell clusters based on gene expression. Within each experiment, there are links to downloadable files, such as RNA quantification matrices, clustering results, reports on protocols and associated metadata, such as assigned cell types.

In a proxy re-encryption scheme a semi-trusted proxy converts a ciphertext for Alice into a ciphertext for Bob without seeing the underlying plaintext. A number of solutions have been proposed in the public-key setting. In this paper, we address the problem of Identity-Based proxy re-encryption, where ciphertexts are transformed from one identity to another. Our schemes are compatible with current IBE deployments and do not require any extra work from the IBE trusted-party key generator. In addition, they are non-interactive and one of them permits multiple re-encryptions. Their security is based on a standard assumption (DBDH) in the random oracle model.

We describe Charm, an extensible framework designed for rapid prototyping of cryptographic systems that utilize the latest advances in cryptography, such as identity and attribute-based encryption, as well as the traditional cryptographic functions. Charm is designed to minimize code complexity, promote code re-use, and to automate interoperability, while not compromising on efficiency.Charm was designed from the ground up to support the implementation of advanced cryptographic schemes. It includes support for multiple cryptographic settings, an extensive library of re-usable code, and a protocol engine to aid in the development of interactive protocols. Our framework also provides a series of specialized tools that enable different cryptosystems to interoperate.We implemented over twenty cryptographic schemes using Charm, including some new ones that to our knowledge have never been built in practice. This paper describes our modular architecture, which includes a built-in benchmarking module that we use to compare the performance of primitives written in Python to comparable C implementations. We show that in many cases our techniques result in a potential order of magnitude decrease in code size, while inducing an acceptable performance impact.