21 research outputs found

    A Framework for Resilient, Transparent, High-throughput, Privacy-Enabled Central Bank Digital Currencies

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    Central Bank Digital Currencies refer to the digitization of lifecycle\u27s of central bank money in a way that meets first of a kind requirements for transparency in transaction processing, interoperability with legacy or new world, and resilience that goes beyond the traditional crash fault tolerant model. This comes in addition to legacy system requirements for privacy and regulation compliance, that may differ from central bank to central bank. This paper introduces a novel framework for Central Bank Digital Currency settlement that outputs a system of record---acting a a trusted source of truth serving interoperation, and dispute resolution/fraud detection needs---, and brings together resilience in the event of parts of the system being compromised, with throughput comparable to crash-fault tolerant systems. Our system further exhibits agnosticity of the exact cryptographic protocol adopted for meeting privacy, compliance and transparency objectives, while ensuring compatibility with the existing protocols in the literature. For the latter, performance is architecturally guaranteed to scale horizontally. We evaluated our system\u27s performance using an enhanced version of Hyperledger Fabric, showing how a throughput of >100K TPS can be supported even with computation-heavy privacy-preserving protocols are in place

    Playing the Wrong Game: An Experimental Analysis of Relational Complexity and Strategic Misrepresentation.

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    It has been suggested that players often produce simplified and/or misspecified mental models of strategic decisions [Kreps, D., 1990. Game Theory and Economic Modeling. Oxford Univ. Press, Oxford]. We submit that the relational structure of players’ preferences in a game is a source of cognitive complexity, and may be an important driver of such simplifications. We provide a classification of order structures in two- person games based on the properties of monotonicity and projectivity, and present experiments in which subjects construct representations of games of different relational complexity and subsequently play the games according to these representations. Experimental results suggest that relational complexity matters. More complex games are harder to represent, and this difficulty seems correlated with short term memory capacity. In addition, most erroneous representations are simpler than the correct ones. Finally, subjects who misrepresent the games behave consistently with such representations, suggesting that in many strategic settings individuals may act optimally on the ground of simplified and mistaken premises

    Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

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    Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.</p

    Sichern der Datenintegrität von Cloud-Speicher bis zu Blockchains

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    Cloud computing has commoditized remote services in recent years. Collaborating on shared data using the cloud has become popular for both personal and professional use. Instead of investing into on-premise infrastructure, low-cost and scalable cloud-based solutions allow to outsource resource-expensive computation and storage. Despite of the benefits those remote services offer, broad adoption still faces security concerns regarding confidentiality and integrity. This thesis addresses the problem of securing data integrity in untrusted environments. Recent trusted execution technology, such as Intel Software Guard Extensions (SGX), aims to overcome the security challenges and seems to pave the way for secure and trustworthy cloud computing. However, when multiple users interact through a potentially misbehaving remote service, consistency violations through rollback and forking attacks can lead to loss of data integrity even when trusted execution is used. In this thesis we identify a number of different settings where consistency violations must be prevented to ensure data integrity. We address these challenges by designing practical solutions that allow to detect a misbehaving remote service. We first focus on securing a commodity cloud storage service where no trusted execution is available and propose a protocol that utilizes the evolution of the storage state to enable the clients to detect integrity and consistency violations. We then introduce trusted execution at the remote service with the intention that this solves the integrity problem; however, a detailed analysis shows that system protection against rollback attacks is challenging. We address this limitation by complementing the system with a distributed protocol to detect rollback attacks. We also propose a system that combines trusted execution with blockchain technology to enhance data protection while resolving difficulties related to rollback attacks. The proposed solutions have been implemented as proofs-of-concept working with real-world systems. Evaluations demonstrate the practicability of these solutions and show that they are direct improvements over previous approaches.Cloud Computing hat in den letzten Jahren Remote Services ("entfernte Dienste") zu Standardprodukten gemacht. Die Zusammenarbeit an gemeinsam genutzten Daten in der Cloud ist sowohl für private als auch für berufliche Anwendungen weit verbreitet. Anstatt in lokale Infrastruktur zu investieren, können mit kostengünstigen und skalierbaren Cloud-Lösungen ressourcenintensive Berechnungen und große Datenmengen ausgelagert werden. Trotz der ökonomischen Vorteile, die solche Dienste bieten, bestehen weiterhin (berechtigte) Sicherheitsbedenken hinsichtlich der Vertraulichkeit und Integrität. Diese Dissertation befasst sich mit dem Problem der Sicherung der Datenintegrität in nicht vertrauenswürdigen Umgebungen. Aktuelle Trusted Execution Environments wie beispielsweise Intel SGX, zielen darauf ab, die heutigen Sicherheitsherausforderungen zu bewältigen, und scheinen den Weg für ein sicheres und vertrauenswürdiges Cloud Computing zu ebnen. Doch wenn mehrere Benutzer über einen möglicherweise bösartigen Remote Service interagieren, können Konsistenzverletzungen durch Rollback- und Forking-Angriffe zu einem Verlust der Datenintegrität führen, selbst wenn Trusted Execution Environments korrekt eingesetzt werden. In dieser Arbeit identifizieren wir eine Reihe verschiedener Situationen, bei denen Konsistenzverletzungen verhindert werden müssen um die Datenintegrität schützen. Wir reduzieren die Folgen dieser Angriffe, indem wir praktische Lösungen entwickeln, mit denen ein Fehlverhalten eines Remote Services erkannt werden kann. Wir widmen uns zuerst der Sicherung eines Cloud-Speicherdienstes und präsentieren ein Protokoll, mit dem Verstöße gegen die Integrität und Konsistenz identifiziert werden können. Anschließend betrachten wir Trusted Execution Environments um das Integritätsproblem zu lösen. Eine vertiefte Analyse zeigt jedoch, dass der Schutz gegen Rollback-Angriffe eine Herausforderung bleibt. Diese Einschränkung adressieren wir durch ein verteiltes Protokoll zur Erkennung solcher Angriffe. Wir präsentieren weiter ein System, das Trusted Execution Environments mit Blockchain-Technologie kombiniert, so den Datenschutz verbessert und Probleme im Zusammenhang mit Rollback-Angriffen löst. Die vorgeschlagenen Lösungen wurden als Proof of Concept für reale Systeme implementiert. Auswertungen demonstrieren die Praktikabilität dieser Lösungen und zeigt, dass sie klare Verbesserungen gegenüber bisherigen Ansätzen mit sich bringen
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