Lynkeus (Italy)

An essential precondition for Artificial Intelligence applications in health is the possibility of having recourse to Big Data. The inconvenient truth, however, is that ever-growing quantities of biomedical data sit idle in fragmented silos, and their use is hindered, among other things, by the need to act in compliance with the General Data Protection Regulation. A recent H2020 Research and Innovation project, MyHealthMyData, highlighted two privacy-enhancing technologies which can provide new solutions for data sharing in health. The first is based on bringing the algorithms to the data in what is called the “visiting mode”, based on secure computation mechanisms, by which only the outcomes are released, not the original data. The second approach leverages the capacity of producing synthetic data through generative adversarial neural networks, adding a further non-reidentification guarantee by making use also of differential privacy. Synthetic data generation and secure computation may thus complement the GDPR by developing an ecosystem in which health data can safely provide the Big Data needed to fully make use of AI in medicine.KeywordsArtificial IntelligenceBig DataAnonymisationPseudonymisationData SharingPersonalised MedicineSecure ComputationSynthetic DataDifferential Privacy

<p>The deliverable represents a reference document at consortium partners’ disposal for day-to-day project management. The document recapitulates project legal and ethical aspects, consortium partner composition and roles, management structure, procedures and tools, reporting guidelines, financial and budget issues, as well as guidelines for dissemination and communication of results.</p>

This document is designed to give guidance on assuring quality in planning, achieving, testing, and refining<br> for each area of the WhoLoDancE project. Although all the areas are closely linked together, the requirements and standard procedures are in many respects different. From a Quality Assurance point of view, it is important to note that the main work of the WhoLoDancE project can be divided into three sections: Work Section 1: Gathering a Collection of Dance Motion making use of Advanced Motion Capture Technology and building Multimodal Rendering, Holographic/volumetric displays for the learning and teaching of dance. Work Section 2: Developing a Database with Data Analytics and Advanced Similarity Search Functionalities making use of innovative Semantic and Emotional Representation Models applied to the data acquired. Work Section 3: Managing the Work and collaboration between the partners and effectively communicate and disseminate the project outcomes. The main purposes of these guidelines are: to define the means of satisfying the objectives for the quality assurance process, and to establish the activities and resources (human organisation, methods and tools) to carry out them; to provide monitoring of all activities, to assure that the project will meet its specified requirements and will be fit-for-use. This guidelines document defines the activities and resources necessary to ensure that the quality requirements of the project are met. It defines quality assurance guidelines and quality assurance activities. It also defines policies for identifying threats on the project and for implementing corrective actions. These guidelines recognise that, with the diversity of the participating project partners, many different quality assurance and control systems are already in place - the guidelines do not, therefore, seek to override existing procedures. This document is applicable to the WhoLoDancE project until its end unless new updates of these QAG are issued in the course of the project.