The Transitivity of Trust Problem in the Interaction of Android Applications

Mobile phones have developed into complex platforms with large numbers of installed applications and a wide range of sensitive data. Application security policies limit the permissions of each installed application. As applications may interact, restricting single applications may create a false sense of security for the end users while data may still leave the mobile phone through other applications. Instead, the information flow needs to be policed for the composite system of applications in a transparent and usable manner. In this paper, we propose to employ static analysis based on the software architecture and focused data flow analysis to scalably detect information flows between components. Specifically, we aim to reveal transitivity of trust problems in multi-component mobile platforms. We demonstrate the feasibility of our approach with Android applications, although the generalization of the analysis to similar composition-based architectures, such as Service-oriented Architecture, can also be explored in the future

Developing Predictive Molecular Maps of Human Disease through Community-based Modeling

The failure of biology to identify the molecular causes of disease has led to disappointment in the rate of development of new medicines. By combining the power of community-based modeling with broad access to large datasets on a platform that promotes reproducible analyses we can work towards more predictive molecular maps that can deliver better therapeutics

Scalable Discovery and Continuous Inventory of Personal Data at Rest in Cloud Native Systems

Cloud native systems are processing large amounts of personal data through numerous and possibly multi-paradigmatic data stores (e.g., relational and non-relational databases). From a privacy engineering perspective, a core challenge is to keep track of all exact locations, where personal data is being stored, as required by regulatory frameworks such as the European General Data Protection Regulation. In this paper, we present Teiresias, comprising i) a workflow pattern for scalable discovery of personal data at rest, and ii) a cloud native system architecture and open source prototype implementation of said workflow pattern. To this end, we enable a continuous inventory of personal data featuring transparency and accountability following DevOps/DevPrivOps practices. In particular, we scope version-controlled Infrastructure as Code definitions, cloud-based storages, and how to integrate the process into CI/CD pipelines. Thereafter, we provide iii) a comparative performance evaluation demonstrating both appropriate execution times for real-world settings, and a promising personal data detection accuracy outperforming existing proprietary tools in public clouds.Comment: Preprint of 2022-09-09 before final copy-editing of an accepted peer-reviewed paper to appear in the Proceedings of the 20th International Conference on Service-Oriented Computing ICSOC 202