No abstract

Business process (BP) stakeholders want to enjoy the benefits of the cloud, but they are also reluctant to expose their BP models which express the know-how of their companies. To prevent such a know-how exposure, this paper proposes a designtime approach for transforming a BP model into BP fragments so that these BP fragments externalized in a multi-cloud context do not allow a cloud resource provider to understand a critical fragment of the company. While existing contributions on this topic remain at the level of principles, we propose an algorithm supporting automatically such a BP model transformation. Index Terms-Business Process; Security Risk Management; Cloud; Privacy; Obfuscation Bank Get Loan Application (GLA) Check Customer Credit (CCC) Risk Evaluation (RE) Risk Capture (RC) Direct Loan Agreement (DLA) Loan Reject (LR) Hierarchy Validation (HV) Decision Consolidation (DC)

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains more than 3,250 DDIs that are consistent with nearly 10,600 PPIs extracted from the IMEx database, and more than 23,000 DDIs (27.5\%) that are consistent with more than 62,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than ten PPIs in the IMEx database are provided. Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.