Message from the EDOC 2021 Workshop and Demo Chairs

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record

To remove or not remove Mobile Apps? A data-driven predictive model approach

Mobile app stores are the key distributors of mobile applications. They regularly apply vetting processes to the deployed apps. Yet, some of these vetting processes might be inadequate or applied late. The late removal of applications might have unpleasant consequences for developers and users alike. Thus, in this work we propose a data-driven predictive approach that determines whether the respective app will be removed or accepted. It also indicates the features' relevance that help the stakeholders in the interpretation. In turn, our approach can support developers in improving their apps and users in downloading the ones that are less likely to be removed. We focus on the Google App store and we compile a new data set of 870,515 applications, 56% of which have actually been removed from the market. Our proposed approach is a bootstrap aggregating of multiple XGBoost machine learning classifiers. We propose two models: user-centered using 47 features, and developer-centered using 37 features, the ones only available before deployment. We achieve the following Areas Under the ROC Curves (AUCs) on the test set: user-centered = 0.792, developer-centered = 0.762

Mayflower - Explorative Modeling of Scientific Workflows with BPEL

Using workflows for scientific calculations, experiments and simulations has been a success story in many cases. Unfortunately, most of the existing scientific workflow systems implement proprietary, non-standardized workflow languages, not taking advantage of the achievements of the conventional business workflow technology. It is only natural to combine these two research branches in order to harness the strengths of both. In this demonstration, we present Mayflower, a workflow environment that enables scientists to model workflows on the fly using extended business workflow technology. It supports the typical trial-and-error approach scientists follow when developing their experiments, computations or simulations and provides scientists with all crucial characteristics of the workflow technology. Additionally, beneficial to the business stakeholders, Mayflower brings additional simplification in workflow development and debugging

Measuring the impact of blockchain on healthcare applications

Blockchain is a technology with potential for making ground breaking steps in addressing social, economic and healthcare challenges. The global information technology scene is being overcrowded with blockchain applications with special focus on the vast healthcare market [12]. The value of information related to healthcare creates a clear path for applying blockchain as a solution for some of the challenges in the healthcare sector, in particular with the goal of creating a fair and transparent way for sharing information and patient data. It is however a fact that while blockchain technology introduces additional complexity to the implementation healthcare software, the benefit the technology actually brings still remains unclear and difficult to evaluate. This vision paper demonstrates our research focus on providing a body of knowledge and tools to help evaluate this impact of blockchain on eHealth applications. In particular, we identify that such a research effort has to explicitly consider cost of addressing challenges inherent to the eHealth domain like integration of disparate software systems (hospitals, research institutions, government agencies, health insurance and pharmaceutical companies, etc.), the potential introduction of cryptocurrencies in healthcare systems, degree of patient service improvement, transparency and compliance to laws and regulations, and others. The more traditional influencing factors, like cost of development and running, licenses for using third-party software services, and the ones inherent to blockchain like cost of computation and energy will also have to be taken into consideration in the metrics model.</p

Poster:Privacy-preserving Genome Analysis using Verifiable Off-Chain Computation

Genome-wide association studies (GWAS) focus on finding associations between genotypes and phenotypes such as susceptibility to diseases. Since genetic data is extremely sensitive and long-lived, individuals and organizations are reluctant to share their data for analysis. This paper proposes two solutions for a fully decentralized and privacy-preserving system for performing minor allele frequency analysis on multiple data sets. Homomorphic encryption and zero-knowledge proofs are used in combination with a blockchain system to achieve data privacy and enable verifiability. Preliminary evaluation of the solutions reveals several important challenges such as handling large cipher texts in smart contracts and reuse of the encrypted data for specific researcher queries that need to be tackled in order to make the solutions more practical.</p

A Taxonomy for Large-Scale Cyber Security Attacks

In an effort to examine the spread of large-scale cyber attacks, researchers have created various taxonomies. These taxonomies are purposefully built to facilitate the understanding and the comparison of these attacks, and hence counter their spread. Yet, existing taxonomies focus mainly on the technical aspects of the attacks, with little or no information about how to defend against them. As such, the aim of this work is to extend existing taxonomies by incorporating new features pertaining the defense strategy, scale, and others. We will compare the proposed taxonomy with existing state of the art taxonomies. We also present the analysis of 174 large cyber security attacks based on our taxonomy. Finally, we present a web tool that we developed to allow researchers to explore exiting data sets of attacks and contribute new ones. We are convinced that our work will allow researchers gain deeper insights into emerging attacks by facilitating their categorization, sharing and analysis, which results in boosting the defense efforts against cyber attack