## 4 Data Treatment
### Technical and Organizational Measures  
The GDPR Regulation Article 32 manages security aspects within personal data processing. With respect to previous regulations, minimal measures (at least within the Italian framework) are not provided anymore, but the responsibilities to adopt all technical and organizational measures are now the responsibility of the data controller, based on the accountability principle (Article 24 and beyond). However, member states are left with some maneuver margins, especially in particular cases, such as those of genetic and health-related data in general. It is often tricky to identify all the risks concerning data processing; among the most useful tools in this respect are those by ENISA, the European Agency for Cybersecurity, and in particular a risk self-assessment that an event, potentially harmful for treatment security, can occur, and the impact it could have on freedom and rights of the interested person, if data confidentiality, integrity and availability are compromised. The measures suggested by this tool are based on those defined for ISO 27,001 certification, and to this extent the impact assessment, requested by the GDPR through Article 35, is pivotal (see Section 4.5):  
* Data confidentiality: Physical environment where servers are located should be made safe in terms of user access through authorization and tracking systems. Communication should also be equipped with Intrusion Detection/Prevention Systems (IDS/IPS) for monitoring network connections, detecting anomalous traffic and acting to notify or limit it. Communication could also be cyphered, and stored data, too. * Pseudonymization: Health360 implements pseudonymization as a technique to join data to each other, especially when collected in the different software modules within the back-end, where personal and identification data are stored within the core module. Disjoint treatment, as required by the regulations, is embedded within the distributed back-end principles, enabled by the employment of the container architecture, simplifying software management of the different modules within different machines. * Multi-factor authentication: Within Health360, this procedure is implemented using an authentication framework, Keycloack, widely used and supported by RedHat. The usage of a framework enables greater scalability with respect to other authentication solutions. An OTP generator is also used, employing commercial solutions (Authy, Google Authenticator, Free OTP, etc.) simply requiring an initial configuration (e.g., through a QR code generated by the platform) with two software systems synchronizing and generating a password depending on a time window. * Roles- and attribute-based authorization (see Figure 3): The Health360 platform aims to collect a great amount of genetic data to improve results obtained by the data mining and Machine Learning algorithms, therefore the access to the platform is limited to only authorized personnel and to the data they have to access. Within the platform, several mechanisms have been developed that allow access to patients for healthcare institutions and, within the institution, depending on the role, keeping active the possibility to execute elaboration algorithms on the whole dataset. Identification information for the patient between different centers is visible just through explicit authorization mechanisms, based on the patient's authorization or in case of necessity and after the registration of the operator allowing the access, with the motivation reported. * Data integrity and availability: Redundancy measures have been implemented within Health360. At first, all modifications performed on data do not modify the existing data, but they add (through an \"append\") the new data, recording the operator identifier and the motivation of the editing procedure. Second, caching technique was applied.