# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 4 Results to the Existing Data Repository  
### Data Sharing  
Typically, technological solutions for data sharing rely on Cloud platforms [43; 44]. In INCISIVE, we start from the assumption that the current solutions available on the market are unable to meet the (conflicting) security requirements of privacy-sensitive applications, such as cancer image processing. INCISIVE aims to extend already-existent solutions of data sharing (e.g., NextCloud [45]) and provide data protection measures and GDPR compliant-by-design tools. In INCISIVE, we aim to enhance the privacy-preserving mechanisms by (i) ensuring that even the public-private Cloud operator has no access to the data (data owners can keep their data completely private); (ii) providing a set of metrics useful to measure the degree of privacy of different categories of data; and (iii) ensuring users have the right and freedom of opting out and maintaining full control of their data. INCISIVE adopts a novel approach for ensuring privacy in already-existent data-sharing platforms, relying on security hardware extensions available in commodity CPUs (most notably Intel Software Guard Extensions, SGX [46]). In INCISIVE, operations of ciphering/deciphering and procedures of key management are protected in the trusted execution environment of Intel SGX. INCISIVE can adopt a blockchain-based auditing mechanism for logging all the critical actions associated with data stored on the Cloud, e.g., patient data access via Cloud operators or other unauthorized users. The adoption of blockchain with built-in smart contracts will ensure the tamper-proof decentralized logging, auditing, and tracking of such actions, leading to traceability and non-repudiation. Moreover, the blockchain mechanism will allow for the logging, auditing, and tracking of transactional data activities between the involved INCISIVE layers (data level, analysis level, and user level), e.g., data submission, sharing, and access in a holistic manner that preserves confidentiality and data access permissions. The impact of such solutions will be tangible via specific privacy metrics, properly designed for the implemented mechanisms. Such metrics are: (i) the attack surface exposed to untrusted software, (ii) the trusted computing base used by security tools in line with source lines of code (SLOC), and (iii) the number of access points to the SGX-isolated environment. With the INCISIVE solution, data owners can avoid trusting the Cloud provider. The threat model covered in this project assumes that even data-sharing platforms could have malicious embedded codes (e.g., backdoors), leading to security flaws. The system is built upon a federated storage approach and a set of standardized open APIs that will enable the linking of various local data sources (similar to the ones utilized in INCISIVE to the system), interaction with users, communication with processing infrastructures, and the sharing of data. The communications with external systems are based on established standards (HL7 FHIR, DICOM, SNOMED, etc.) and in combination with mapping (e.g., common data models), and the data management functionalities interoperability and interconnectivity are ensured. The system is considered one of the main exploitable outcomes of the INCISIVE project. Its stand-alone nature and interoperability features will enable its connection with third-party trusted AI providers, as well as with established healthcare systems (e.g., PIMED in Catalonia, etc. ), contributing to its future sustainability. The development of new AI methods and applications for image analysis requires large-scale datasets for the training of machine learning algorithms, as classification accuracy is largely dependent on the size and quality of the dataset. Currently, cancer imaging databases are scattered around the EU with no joint coordination across them, being small in size and lacking real-world variation.