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When transferring sensitive data to a non-trusted party, end-users require that the data be kept private. Mobile and IoT application developers want to leverage the sensitive data to provide better user experience and intelligent services. Unfortunately, existing programming abstractions make it impossible to reconcile these two seemingly conflicting objectives. In this paper, we present a novel programming mechanism for distributed managed execution environments that hides sensitive user data, while enabling developers to build powerful and intelligent applications, driven by the properties of the sensitive data. Specifically, the sensitive data is never revealed to clients, being protected by the runtime system. Our abstractions provide declarative and configurable data query interfaces, enforced by a lightweight distributed runtime system. Developers define when and how clients can query the sensitive data’s properties (i.e., how long the data remains accessible, how many times its properties can be queried, which data query methods apply, etc.). Based on our evaluation, we argue that integrating our novel mechanism with the Java Virtual Machine (JVM) can address some of the most pertinent privacy problems of IoT and mobile applications. 

By processing sensory data in the vicinity of its generation, edge computing reduces latency, improves responsiveness, and saves network bandwidth in data-intensive applications. However, existing edge computing solutions operate under the assumption that the edge infrastructure will comprise a set of pre-deployed, custom-configured computing devices, connected by a reliable local network. Although edge computing has great potential to provision the necessary computational resources in highly dynamic and volatile environments, including disaster recovery scenes and wilderness expeditions, extant distributed system architectures in this domain are not resilient against partial failure, caused by network disconnections. In this paper, we present a novel edge computing system architecture that delivers failure-resistant and efficient applications by dynamically adapting to handle failures; if the edge server becomes unreachable, device clusters start executing the assigned tasks by communicating P2P, until the edge server becomes reachable again. Our experimental results with the reference implementation show high responsiveness and resilience in the face of partial failure. These results indicate that the presented solution can integrate the individual capacities of mobile devices into powerful edge clouds, providing efficient and reliable services for end-users in highly dynamic and volatile environments.