Innovative Verfahren für die standortübergreifende Datennutzung in der medizinischen Forschung

Implementing modern data-driven medical research approaches ("Artificial intelligence", "Data Science") requires access to large amounts of data ("Big Data"). Typically, this can only be achieved through cross-institutional data use and exchange ("Data Sharing"). In this process, the protection of the privacy of patients and probands affected is a central challenge. Various methods can be used to meet this challenge, such as anonymization or federation. However, data sharing is currently put into practice only to a limited extent, although it is demanded and promoted from many sides. One reason for this is the lack of clarity about the advantages and disadvantages of different data sharing approaches. The first goal of this thesis was to develop an instrument that makes these advantages and disadvantages more transparent. The instrument systematizes approaches based on two dimensions - utility and protection - where each dimension is further differentiated with three axes describing different aspects of the dimensions, such as the degree of privacy protection provided by the results of performed analyses or the flexibility of a platform regarding the types of analyses that can be performed. The instrument was used for evaluation purposes to analyze the status quo and to identify gaps and potentials for innovative approaches. Next, and as a second goal, an innovative tool for the practical use of cryptographic data sharing methods has been designed and implemented. So far, such approaches are only rarely used in practice due to two main obstacles: (1) the technical complexity of setting up a cryptography-based data sharing infrastructure and (2) a lack of user-friendliness of cryptographic data sharing methods, especially for medical researchers. The tool EasySMPC, which was developed as part of this work, is characterized by the fact that it allows cryptographically secure computation of sums (e.g., frequencies of diagnoses) across institutional boundaries based on an easy-to-use graphical user interface. Neither technical expertise nor the deployment of specific infrastructure components is necessary for its practical use. The practicability of EasySMPC was analyzed experimentally in a detailed performance evaluation.Moderne datengetriebene medizinische Forschungsansätze („Künstliche Intelligenz“, „Data Science“) benötigen große Datenmengen („Big Data“). Dies kann im Regelfall nur durch eine institutionsübergreifende Datennutzung erreicht werden („Data Sharing“). Datenschutz und der Schutz der Privatsphäre der Betroffenen ist dabei eine zentrale Herausforderung. Um dieser zu begegnen, können verschiedene Methoden, wie etwa Anonymisierungsverfahren oder föderierte Auswertungen, eingesetzt werden. Allerdings findet Data Sharing in der Praxis nur selten statt, obwohl es von vielen Seiten gefordert und gefördert wird. Ein Grund hierfür ist die Unklarheit ¸über Vor- und Nachteile verschiedener Data Sharing-Ansätze. Erstes Ziel dieser Arbeit war es, ein Instrument zu entwickeln, welches diese Vor- und Nachteile transparent macht. Das Instrument bewertet Ansätze anhand von zwei Dimensionen - Nutzen und Schutz - wobei jede Dimension mit drei Achsen weiter differenziert ist. Die Achsen bestehen etwa aus dem Grad des Schutzes der Privatsphäre, der durch die Ergebnisse der durchgeführten Analysen gewährleistet wird oder der Flexibilität einer Plattform hinsichtlich der Arten von Analysen, die durchgeführt werden können. Das Instrument wurde zu Evaluationszwecken für die Analyse des Status Quo sowie zur Identifikation von Lücken und Potenzialen für innovative Verfahren eingesetzt. Als zweites Ziel wurde anschließend ein innovatives Werkzeug für den praktischen Einsatz von kryptographischen Data Sharing-Verfahren entwickelt. Der Einsatz entsprechender Ansätze scheitert bisher vor allem an zwei Barrieren: (1) der technischen Komplexität beim Aufbau einer Kryptographie-basierten Data Sharing-Infrastruktur und (2) der Benutzerfreundlichkeit kryptographischer Data Sharing-Verfahren, insbesondere für medizinische Forschende. Das neue Werkzeug EasySMPC zeichnet sich dadurch aus, dass es eine kryptographisch sichere Berechnung von Summen (beispielsweise Häufigkeiten von Diagnosen) über Institutionsgrenzen hinweg auf Basis einer einfach zu bedienenden graphischen Benutzeroberfläche ermöglicht. Zur Anwendung ist weder technische Expertise noch der Aufbau spezieller Infrastrukturkomponenten notwendig. Die Praxistauglichkeit von EasySMPC wurde in einer ausführlichen Performance-Evaluation experimentell analysiert

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

Background: Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods: The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results: Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions: There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken

Enabling open science in medicine through data sharing: an overview and assessment of common approaches from the European perspective

Open Science involves the sharing of knowledge and data as well as the exchange of research results. This is particularly important in the biomedical field, as it can foster validation studies in response to the replication crisis and improve resource utilisation. Since medical data is particularly privacy sensitive, its processing is subject to strong data protection requirements. Agencies, institutions, and projects in the European Union are still struggling with the establishment of widely accepted mechanisms supporting the sharing of data for Open Science practices. The goal of this paper is to provide an overview of different methods that have been used for this purpose and to discuss their technical properties and legal challenges. Our assessment is based on well-known conceptualizations, such as the Five Safes Framework. The result shows that different approaches provide different trade-offs between the functionalities and the degree of data protection provided, and that there are open legal issues. Current legislative initiatives in the EU, including regulations for the European Health Data Space and the Data Governance Act, have the potential to address some of the resulting uncertainties

Enabling Open Science in Medicine Through Data Sharing: An Overview and Assessment of Common Approaches from the European Perspective

Open Science involves the sharing of knowledge and data as well as the exchange of research results. This is particularly important in the biomedical field, as it can foster validation studies in response to the replication crisis and improve resource utilisation. Since medical data is particularly privacy sensitive, its processing is subject to strong data protection requirements. Agencies, institutions, and projects in the European Union are still struggling with the establishment of widely accepted mechanisms supporting the sharing of data for Open Science practices. The goal of this paper is to provide an overview of different methods that have been used for this purpose and to discuss their technical properties and legal challenges. Our assessment is based on well-known conceptualizations, such as the Five Safes Framework. The result shows that different approaches provide different trade-offs between the functionalities and the degree of data protection provided, and that there are open legal issues. Current legislative initiatives in the EU, including regulations for the European Health Data Space and the Data Governance Act, have the potential to address some of the resulting uncertainties

Protein modification with ISG15 blocks coxsackievirus pathology by antiviral and metabolic reprogramming

Protein modification with ISG15 (ISGylation) represents a major type I IFN–induced antimicrobial system. Common mechanisms of action and species-specific aspects of ISGylation, however, are still ill defined and controversial. We used a multiphasic coxsackievirus B3 (CV) infection model with a first wave resulting in hepatic injury of the liver, followed by a second wave culminating in cardiac damage. This study shows that ISGylation sets nonhematopoietic cells into a resistant state, being indispensable for CV control, which is accomplished by synergistic activity of ISG15 on antiviral IFIT1/3 proteins. Concurrent with altered energy demands, ISG15 also adapts liver metabolism during infection. Shotgun proteomics, in combination with metabolic network modeling, revealed that ISG15 increases the oxidative capacity and promotes gluconeogenesis in liver cells. Cells lacking the activity of the ISG15-specific protease USP18 exhibit increased resistance to clinically relevant CV strains, therefore suggesting that stabilizing ISGylation by inhibiting USP18 could be exploited for CV-associated human pathologies

CALYPSO 2019 Cruise Report: field campaign in the Mediterranean

This cruise aimed to identify transport pathways from the surface into the interior ocean during the late winter in the Alborán sea between the Strait of Gibraltar (5°40’W) and the prime meridian. Theory and previous observations indicated that these pathways likely originated at strong fronts, such as the one that separates salty Mediterranean water and the fresher water in owing from the Atlantic. Our goal was to map such pathways and quantify their transport. Since the outcropping isopycnals at the front extend to the deepest depths during the late winter, we planned the cruise at the end of the Spring, prior to the onset of thermal stratification of the surface mixed layer.Funding was provided by the Office of Naval Research under Contract No. N000141613130

Effects of Video-Based Visual Training on Decision-Making and Reactive Agility in Adolescent Football Players

This study investigated the trainability of decision-making and reactive agility via video-based visual training in young athletes. Thirty-four members of a national football academy (age: 14.4 ± 0.1 years) were randomly assigned to a training (VIS; n = 18) or a control group (CON; n = 16). In addition to the football training, the VIS completed a video-based visual training twice a week over a period of six weeks during the competition phase. Using the temporal occlusion technique, the players were instructed to react on one-on-one situations shown in 40 videos. The number of successful decisions and the response time were measured with a video-based test. In addition, the reactive-agility sprint test was used. VIS significantly improved the number of successful decisions (22.2 ± 3.6 s vs. 29.8 ± 4.5 s; p < 0.001), response time (0.41 ± 0.10 s vs. 0.31 ± 0.10 s; p = 0.006) and reactive agility (2.22 ± 0.33 s vs. 1.94 ± 0.11 s; p = 0.001) pre- vs. post-training. No significant differences were found for CON. The results have shown that video-based visual training improves the time to make decisions as well as reactive agility sprint-time, accompanied by an increase in successful decisions. It remains to be shown whether or not such training can improve simulated or actual game performance

EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation

BACKGROUND Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability. RESULTS To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor. CONCLUSIONS We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice

SOCIB INT RadarAPM Jul2016. Lagrangian Experiment Ibiza Channel

Lagrangian experiment in the Ibiza channel during Summer 2016, aiming to validate HF-Radar surface currents (new Antenna Pattern Measurement