EARS: Cloud-based Oblivious Spectrum Mapping and Allocation

University Of Florida, Gainesville FL

Investigators

Abstract

Dynamic spectrum access (DSA) techniques offer the potential to use the radio spectrum more efficiently by allowing radios to sense which parts of the radio spectrum are not currently being used by the licensed users or other opportunistic users. However, detecting the presence of transmissions in a particular band is difficult because of randomness in the radio propagation environment and because radio signals can be received at very low power levels. Sensing the radio spectrum can be made more accurate by collecting and processing the sensing information from multiple radios. However, in combining this information, the locations and characteristics of the sensing radios may be revealed to the other radios or to various companies involved in collecting and combining the information. This research project will develop novel techniques to protect the privacy of users involved in sensing the radio spectrum as part of a DSA system. The techniques are built on top of secure computing primitives, such as garbled circuits, which uses cryptographic techniques to allow users to compute a result without any of the parties being able to know the other parties inputs to the computation. Although complete privacy is not possible in a spectrum sensing system, this project aims to develop systems that achieve k-anonymity, in which a user's location and capabilities may only be reduced to one of k possibilities. A significant challenge in developing such techniques is that many secure computing primitives require high computational complexity, and thus cannot be implemented on many DSA devices, such as future generations of cellular phones. Thus, the project will develop privacy-preserving spectrum sensing techniques that have sufficiently low complexity to be implemented on such devices. One of the approaches to achieving this is to partition the computation between the devices and a cloud-computing server. These techniques will be implemented on a software-defined radio testbed that interfaces with a commercial cloud computing resource to allow testing using real radio signals and real computing platforms.