5G will lay the foundations for the mainstream broadband wireless technology of the next decade, a leverage toward ensuring the eficiency, effectiveness and adaptability of everyday high-demanding operations, such as those in Public Protection and Disaster Relief. The ITU considers LTE-Advanced systems and 5G as a mission critical PPDR technology able to address the needs of MC intelligence, providing support for MC voice, data and video services as an IMT radio interface. In this paper, we introduce 5G-EPICENTRE, an innovation action funded by the EC under the Horizon 2020 research framework, which aims to deliver an open, federated 5G end-toend experimentation platform specifically tailored to the needs of PPDR software solutions. The envisioned platform will allow SMEs and developers a lower entry barrier to the PPDR market, enabling them to build-up and experiment with their solutions in a cost effective way. The 5G-EPICENTRE platform will be based on an open Service Oriented Architecture and will accommodate open access to 5G networks' resources, acting this way as an open source repository for PPDR 5G Network Applications (NetApps). The purpose of the federated platform is to provide sufficient resources to cover the entire range of the 3 ITU-defined service types (i.e. eMBB, mMTC and URLLC) and to deliver secure interoperability capabilities beyond vendor-specific implementation.

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Personal data is a necessity in many fields for research and innovation purposes, and when such data is shared, the data controller carries the responsibility of protecting the privacy of the individuals contained in their dataset. The removal of direct identifiers, such as full name and address, is not enough to secure the privacy of individuals as shown by de-anonymisation methods in the scientific literature. Data controllers need to become aware of the risks of de-anonymisation and apply the appropriate anonymisation measures before sharing their datasets, in order to comply with privacy regulations. To address this need, we defined a procedure that makes data controllers aware of the de-anonymisation risks and helps them in deciding the anonymisation measures that need to be taken in order to comply with the General Data Protection Regulation (GDPR). We showcase this procedure with a customer relationship management (CRM) dataset provided by a telecommunications provider. Finally, we recount the challenges we identified during the definition of this procedure and by putting existing knowledge and tools into practice.