It is well known that observability is closely related to vulnerability against confidentiality attacks: once a low level process is able to observe differences in the behaviour of a high level process it is able to deduce information about its secrets, and confidentiality is thus at least partially violated. However, if whatever the high level secrets are, the behaviours of the observed proces...

We present a traffic analysis of the ADU anonymity scheme presented at ESORICS 2008, and the related RADU scheme. We show that optimal attacks are able to de-anonymize messages more effectively than believed before. Our analysis applies to single messages as well as long term observations using multiple messages. The search of a “better” scheme is bound to fail, since we prove that the original...

Model checking can tell us whether a system is correct; probabilistic model checking can also tell us whether a system is timely and reliable. Moreover, probabilistic model checking allows one to verify properties that may not be true with probability one, but may still hold with an acceptable probability. The challenge in developing a probabilistic model checker able to handle realistic system...

Notch ligands and receptors have been implicated in helper T cell (Th cell) differentiation. Whether Notch signals are involved in differentiation of T helper type 1 (Th1) cells, Th2 cells, or both, however, remains unresolved. To clarify the role of Notch in Th cell differentiation, we generated mice that conditionally inactivate Notch signaling in mature T cells. Mice that lack Notch signalin...

In this paper, we revisit the construction of fail-stop signatures from the factoring assumption. These signatures were originally proposed to provide information-theoretic-based security against forgeries. In contrast to classical signature schemes, in which signers are protected through a computational conjecture, fail-stop signature schemes protect the signers in an information theoretic sen...

Discovering Probabilistic Frequent Itemsets (PFI) is very challenging since algorithms designed for deterministic data are not applicable in probabilistic data. The problem is even more difficult for probabilistic data streams where massive frequent updates need to be taken into account while respecting data stream constraints. In this paper, we propose FEMP (Fast and Exact Mining of Probabilis...

Linear regression is arguably one of the most widely used statistical methods. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs) in the linear regression context. In general, the IM framework is able to produce meaningful probabilistic summaries of the sta...

This paper describes a method for classifying and tracking multiple moving objects with a laser range finder (LRF). As moving objects are tracked in the framework of sample-based joint probabilistic data association filters (SJPDAFs), the proposed method is robust against occlusions or false segmentation of LRF scans. It divides tracking targets and corresponding LRF segments into clusters and ...