Abstract. Without trust, pervasive devices cannot collaborate effectively, and without collaboration, the pervasive computing vision cannot be made a reality. Distributed trust frameworks may support trust and thus foster collaboration in an hostile pervasive computing environment. Existing frameworks deal with foundational properties of computational trust. We here propose a distributed trust framework that satisfies a broader range of properties. Our framework: (i) evolves trust based on a Bayesian formalization, whose trust metric is expressive, yet tractable; (ii) is lightweight; (iii) protects user anonymity, whilst being resistant to “Sybil attacks ” (and enhancing detection of two collusion attacks); (iv) integrates a risk-aware decision module. We evaluate the framework through four experiments. 1

We study the vulnerability of several implementations of the Data Encryption Standard (DES) cryptosystem under a timing attack. A timing attack is a method designed to break cryptographic systems that was recently proposed by Paul Kocher. It exploits the engineering aspects involved in the implementation of cryptosystems and might succeed even against cryptosystems that remain impervious to sophisticated cryptanalytic techniques. A timing attack is, essentially, a way of obtaining some user's private information by carefully measuring the time it takes the user to carry out cryptographic operations. In this work we analyze two implementations of DES. We show that a timing attack yields the Hamming weight of the key used by both DES implementations. Moreover, the attack is computationally inexpensive. We also show that all the design characteristics of the target system, necessary to carry out the timing attack, can be inferred from timing measurements.

This paper introduces a novel method of rights protection for categorical data through watermarking. We discover new watermark embedding channels for relational data with categorical types. We design novel watermark encoding algorithms and analyze important theoretical bounds including mark vulnerability. While fully preserving data quality requirements, our solution survives important attacks, such as subset selection and data re-sorting. Mark detection is fully “blind ” in that it doesn’t require the original data, an important characteristic especially in the case of massive data. We propose various improvements and alternative encoding methods. We perform validation experiments by watermarking the outsourced Wal-Mart sales data available at our institute. We prove (experimentally and by analysis) our solution to be extremely resilient to both alteration and data loss attacks, for example tolerating up to 80% data loss with a watermark alteration of only 25%. 1

Prior work has shown that the global earliest-deadline-first (GEDF) scheduling algorithm ensures bounded deadline tardiness on multiprocessors with no utilization loss; therefore, GEDF may be a good candidate scheduling algorithm for soft real-time workloads. However, such workloads are often implemented assuming an average-case provisioning, and in prior tardiness-bound derivations for GEDF, worst-case execution costs are assumed. As worst-case costs can be orders of magnitude higher than average-case costs, using a worstcase provisioning may result in significant wasted processing capacity. In this paper, prior tardiness-bound derivations for GEDF are generalized so that execution times are probabilistic, and a bound on expected (mean) tardiness is derived. It is shown that, as long as the total expected utilization is strictly less than the number of available processors, the expected tardiness of every task is bounded under GEDF. The result also implies that any quantile of the tardiness distribution is also bounded. 1

The traditional 2nd-order advection-dispersion equation (ADE) does not adequately describe the movement of solute tracers in aquifers. This study examines and re-derives the governing equation. The analysis starts with a generalized notion of particle movements, since the second-order equation is trying to impart Brownian motion on a mathematical plume at any time. If particle motions with long-range spatial correlation are more favored, then the motion is described by Lévy's family of α-stable densities. The new governing (Fokker-Planck) equation of these motions is similar to the ADE except that the order (α) of the highest derivative is fractional (e.g., the 1.65th derivative). Fundamental solutions resemble the Gaussian except that they spread proportional to time 1/α and have heavier tails. The order of the fractional ADE (FADE) is shown to be related to the aquifer velocity autocorrelation function. The FADE derived here is used to....

Abstract. The key challenge in the design of wireless sensor networks is maximizing their lifetime. The information about the amount of available energy in each part of the network is called the energy map and can be useful to increase the lifetime of the network. In this paper, we address the problem of constructing the energy map of a wireless sensor network using prediction-based approaches. We also present an energy dissipation model that is used to simulate the behavior of a sensor node in terms of energy consumption. Simulation results compare the performance of the prediction-based approaches with a naive one in which no prediction is used. The results show that the prediction-based approaches outperform the naive in a variety of parameters.

this paper uses a straightforward program synthesis technique: a compression plan, consisting of instructions for each block of input data, is generated, guided by the statistical properties of the input data. Because of its use of algorithms specifically suited to the types of redundancy exhibited by the particular input file, the system achieves consistent average performance throughout the file, as shown by experimental evidence

Factored Edge-Valued Binary Decision Diagrams form an extension to Edge-Valued Binary Decision Diagrams. By associating both an additive and a multiplicative weight with the edges, FEVBDDs can be used to represent a wider range of functions concisely. As a result, the computational complexity for certain operations can be significantly reduced compared to EVBDDs. Additionally, the introduction of multiplicative edge weights allows us to directly represent the so-called complement edges which are used in OBDDs, thus providing a one to one mapping of all OBDDs to FEVBDDs. Applications such as integer linear programming and logic verification that have been proposed for EVBDDs also benefit from the extension. We present a complete matrix package based on FEVBDDs and apply the package to the problem of solving the Chapman-Kolmogorov equations.

MR images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar ...

The key challenge in the design of wireless sensor networks is maximizing its lifetime. The information of the amount of available energy in each part of the network is called energy map and can be useful to increase the lifetime of the network. In this paper, we discuss the problem of predicting the energy consumption in wireless sensor networks. We propose a Network State Model to represent the behavior of a sensor node and show how this model can be used to predict the energy consumption by a sensor node and consequently to construct an energy map of a wireless sensor network. We show simulation results that compare the error of our approach with the naive one in which each node sends periodically its energy reserves to the monitoring node. The results show that our approach outperforms the naive one specially in peak-load areas.