Towards Safer Information Sharing in the Cloud

Web interactions usually require the exchange of personal and confidential information for a variety of purposes, including enabling business transactions and the provisioning of services. A key issue affecting these interactions is the lack of trust and control on how data is going to be used and processed by the entities that receive it. In the traditional world, this problem is addressed by using contractual agreements, those are signed by the involved parties, and law enforcement. This could be done electronically as well but, in ad- dition to the trust issue, there is currently a major gap between the definition of legal contracts regulat- ing the sharing of data, and the software infrastructure required to support and enforce them. How to enable organisations to provide more automation in this pro- cess? How to ensure that legal contracts can be actually enforced by the underlying IT infrastructure? How to enable end-users to express their preferences and con- straints within these contracts? This article describes our R&D work to make progress towards addressing this gap via the usage of electronic Data Sharing Agree- ments (e-DSA). The aim is to share our vision, discuss the involved challenges and stimulate further research and development in this space. We specifically focus on a cloud scenario because it provides a rich set of?use cases involving interactions and information shar- ing among multiple stakeholders, including users and service providers.?

Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery

Recent research in representation learning utilizes large databases of proteins or molecules to acquire knowledge of drug and protein structures through unsupervised learning techniques. These pre-trained representations have proven to significantly enhance the accuracy of subsequent tasks, such as predicting the affinity between drugs and target proteins. In this study, we demonstrate that by incorporating knowledge graphs from diverse sources and modalities into the sequences or SMILES representation, we can further enrich the representation and achieve state-of-the-art results on established benchmark datasets. We provide preprocessed and integrated data obtained from 7 public sources, which encompass over 30M triples. Additionally, we make available the pre-trained models based on this data, along with the reported outcomes of their performance on three widely-used benchmark datasets for drug-target binding affinity prediction found in the Therapeutic Data Commons (TDC) benchmarks. Additionally, we make the source code for training models on benchmark datasets publicly available. Our objective in releasing these pre-trained models, accompanied by clean data for model pretraining and benchmark results, is to encourage research in knowledge-enhanced representation learning

A Design Phase for Data Sharing Agreements

The number of factories, service providers, retailers, and final users that create networks and establish collaborations for increasing their productivity and competitiveness is constantly growing, especially by effect of the globalization and outsourcing of industrial activities. This trend introduces new complexities in the value supply chain, not last the need for secure and private data sharing among the collaborating parties. A Data Sharing Agreement (DSA) represents a flexible mean to assure privacy and security of electronic data exchange. DSA is a formal document regulating data exchange in a controlled manner, by defining a set of policies specifying what parties are allowed, or required, or denied to do with respect to data covered by the agreement. A key factor in the adoption of DSAs is their usability. Here, we propose an approach for a consistent and automated design phase of the agreements. In particular, we present an authoring tool for a user-friendly and cooperative editing of DSA and an analysis tool to identify possible conflicts or incompatibilities among the DSA policies