Visible to the public Querying Invisible Objects: Supporting Data-Driven, Privacy-Preserving Distributed Applications

TitleQuerying Invisible Objects: Supporting Data-Driven, Privacy-Preserving Distributed Applications
Publication TypeConference Paper
Year of Publication2017
AuthorsLiu, Yin, Song, Zheng, Tilevich, Eli
Conference NameProceedings of the 14th International Conference on Managed Languages and Runtimes
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5340-3
Keywordscomposability, data privacy, Data-Intensive Applications, Human Behavior, Metrics, privacy, Programming Abstractions, pubcrawl, resilience, Resiliency, Virtual Machine Design, virtualization privacy

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.

Citation Keyliu_querying_2017