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.
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A Privacy Negotiation Mechanism for IoT
This paper presents a new privacy negotiation mechanism for an IoT environment that is both efficient and practical to cope with the IoT special need of seamlessness. This mechanism allows IoT users to express and enforce their personal privacy preferences in a seamless manner while interacting with IoT deployments. In addition, the proposed mechanism satisfies the privacy requirements of the IoT deployment owner. Finally, the proposed privacy mechanism is agnostic to the actual IoT architecture and can be used over a user-managed, edge-managed or a cloud-managed IoT architecture. Prototypes of the proposed mechanism have been implemented for each of these three architectures, and the results show the capability of the protocol to negotiate privacy while adding insignificant time overhead.
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- Award ID(s):
- 1816379
- PAR ID:
- 10120927
- Date Published:
- Journal Name:
- The 16th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC 2018)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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