Approaches to Semantic Web Services: An Overview and Comparison

Abstract. The next Web generation promises to deliver Semantic Web Services (SWS); services that are self-described and amenable to automated discovery, composition and invocation. A prerequisite to this, however, is the emergence and evolution of the Semantic Web, which provides the infrastructure for the semantic interoperability of Web Services. Web Services will be augmented with rich formal descriptions of their capabilities, such that they can be utilized by applications or other services without human assistance or highly constrained agreements on interfaces or protocols. Thus, Semantic Web Services have the potential to change the way knowledge and business services are consumed and provided on the Web. In this paper, we survey the state of the art of current enabling technologies for Semantic Web Services. In addition, we characterize the infrastructure of Semantic Web Services along three orthogonal dimensions: activities, architecture and service ontology. Further, we examine and contrast three current approaches to SWS according to the proposed dimensions

A Hybrid Approach to Privacy-Preserving Federated Learning

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions

A standard-driven communication protocol for disconnected clinics in rural areas

The importance of the Electronic Health Record (EHR), which stores all healthcare-related data belonging to a patient, has been recognized in recent years by governments, institutions, and industry. Initiatives like Integrating the Healthcare Enterprise (IHE) have been developed for the definition of standard methodologies for secure and interoperable EHR exchanges among clinics and hospitals. Using the requisites specified by these initiatives, many large-scale projects have been set up to enable healthcare professionals to handle patients' EHRs. Applications deployed in these settings are often considered safety-critical, thus ensuring such security properties as confidentiality, authentication, and authorization is crucial for their success. In this paper, we propose a communication protocol, based on the IHE specifications, for authenticating healthcare professionals and assuring patients' safety in settings where no network connection is available, such as in rural areas of some developing countries. We define a specific threat model, driven by the experience of use cases covered by international projects, and prove that an intruder cannot cause damages to the safety of patients and their data by performing any of the attacks falling within this threat model. To demonstrate the feasibility and effectiveness of our protocol, we have fully implemented it