Architectural Runtime Models for Privacy Checks of Cloud Applications

Abstract

Cloud providers as well as cloud customers are obliged to comply with privacy regulations. In particular, these regulations prescribe compliance to geo-location policies that define at which geographical locations personal data may be stored or processed. However, cloud elasticity dynamically adapts computing resources to workload changes by replicating and migrating components as well as included data among data centers. As a result, data might be moved to different geographical locations, thereby violating geo-location policies. Current approaches for cloud monitoring and compliance fall short in detecting relevant cases of such policy violations, particularly cases that involve data transfers among data centers. We address this gap by exploiting runtime models for the analysis of privacy violations during runtime. In this paper, we introduce architectural runtime models that reflect information about application components, their interactions, and their cloud deployments. We combine push-based heartbeat monitoring with event processing, and graph grammars for efficiently updating those models. An empirical evaluation based on a prototypical implementation applied to Amazon EC2 and the CoCoME case study indicates that the runtime model approach accurately and efficiently reflects changes of cloud applications.

Modeling Data Protection Vulnerabilities of Cloud Systems Using Risk Patterns

“…Similarly to our approach, the work of Schmieders et al also applied modelbased adaptive methods to data protection in the cloud [24,25]. That work, however, is limited to one specific type of privacy goals: geo-location constraints.…”