Biblio

Cooperative spectrum sensing is often necessary in cognitive radios systems to localize a transmitter by fusing the measurements from multiple sensing radios. However, revealing spectrum sensing information also generally leaks information about the location of the radio that made those measurements. We propose a protocol for performing cooperative spectrum sensing while preserving the privacy of the sensing radios. In this protocol, radios fuse sensing information through a distributed particle filter based on a tree structure. All sensing information is encrypted using public-key cryptography, and one of the radios serves as an anonymizer, whose role is to break the connection between the sensing radios and the public keys they use. We consider a semi-honest (honest-but-curious) adversary model in which there is at most a single adversary that is internal to the sensing network and complies with the specified protocol but wishes to determine information about the other participants. Under this scenario, an adversary may learn the sensing information of some of the radios, but it does not have any way to tie that information to a particular radio's identity. We test the performance of our proposed distributed, tree-based particle filter using physical measurements of FM broadcast stations.

Building secure systems used to mean ensuring a secure perimeter, but that is no longer the case. Today's systems are ill-equipped to deal with attackers that are able to pierce perimeter defenses. Data provenance is a critical technology in building resilient systems that will allow systems to recover from attackers that manage to overcome the "hard-shell" defenses. In this paper, we provide background information on data provenance, details on provenance collection, analysis, and storage techniques and challenges. Data provenance is situated to address the challenging problem of allowing a system to "fight-through" an attack, and we help to identify necessary work to ensure that future systems are resilient.