A forensically-enabled IASS cloud computing architecture

Current cloud architectures do not support digital forensic investigators, nor comply with today’s digital forensics procedures largely due to the dynamic nature of the cloud. Whilst much research has focused upon identifying the problems that are introduced with a cloud-based system, to date there is a significant lack of research on adapting current digital forensic tools and techniques to a cloud environment. Data acquisition is the first and most important process within digital forensics – to ensure data integrity and admissibility. However, access to data and the control of resources in the cloud is still very much provider-dependent and complicated by the very nature of the multi-tenanted operating environment. Thus, investigators have no option but to rely on cloud providers to acquire evidence, assuming they would be willing or are required to by law. Furthermore, the evidence collected by the Cloud Service Providers (CSPs) is still questionable as there is no way to verify the validity of this evidence and whether evidence has already been lost. This paper proposes a forensic acquisition and analysis model that fundamentally shifts responsibility of the data back to the data owner rather than relying upon a third party. In this manner, organisations are free to undertaken investigations at will requiring no intervention or cooperation from the cloud provider. The model aims to provide a richer and complete set of admissible evidence than what current CSPs are able to provide

The Lawyer\u27s Duty of Disclosure Ethics and Sarbanes-Oxley the New Conundrum for Patent Lawyers

The general purpose of this paper is to sensitize intellectual property lawyers to the potential impact on their practice created by Sarbanes-Oxley. At a more detailed level, and because of the unique challenges facing them, this paper addresses Sarbanes-Oxley\u27s potential impact on patent lawyers who practice before the PTO, even when it is the patent lawyer\u27s sole practice. To that end, this paper will highlight relevant portions of Rule 56, the relevant ethical code sections, and the pertinent considerations under Sarbanes-Oxley

Class groups of characteristic-p function field analogues of Q(n^(1/p))

In the theory of cyclotomic function fields, the Carlitz module $\Lambda_M$ associated to a polynomial $M$ in a global function field of characteristic $p$ provides a strong analogy to the roots of unity $\mu_p$ in a number field. In this work, we consider a natural extension of this theory to give a compatible analogue of the $p$-th root of an integer $n$. The most fundamental case, and the one which most closely mimics the number field situation, is when the Carlitz module is defined by a linear polynomial (which can be assumed to be $T$) in $k={\mathbb F}_q(T)$. The Carlitz module $\Lambda_T$ generates a degree-$(q-1)$ extension $k(\Lambda_T)$ which shares many properties with the field ${\mathbb Q}(\mu_p)$, where $\mu_p$ is the module of $p$-th roots of unity. To form the analogue of ${\mathbb Q}(\sqrt[p]{n})$, we define a degree-$q$ extension $F/k$ associated to a polynomial $P(T) \in k$, for which the normal closure is formed by adjoining $\Lambda_T$. In the introduction, we describe in detail the parallels between this construction and that in the number field setting. We then compute the class number $h_F$ for a large number of such fields. The remainder of the work is concerned with proving results about the class groups and class numbers of this family of fields. These are:\begin{itemize} \item a formula relating the class number of $F$ to that of its normal closure, along with a theorem about the structure of the class group of the normal closure \item a formula relating the class number of a compositum of such $F$ to the class numbers of the constituent fields \item conditions on $P(T)$ for when the characteristic, $p$, of $F$ divides its class number, along with bounds on the rank of the $p$-part of the class group. \end{itemize

A Bayesian Semiparametric Method For Estimating Causal Quantile Effects

Standard causal inference characterizes treatment effect through averages, but the counterfactual distributions could be different in not only the central tendency but also spread and shape. To provide a comprehensive evaluation of treatment effects, we focus on estimating quantile treatment effects (QTEs). Existing methods that invert a nonsmooth estimator of the cumulative distribution functions forbid inference on probability density functions (PDFs), but PDFs can reveal more nuanced characteristics of the counterfactual distributions. We adopt a semiparametric conditional distribution regression model that allows inference on any functionals of counterfactual distributions, including PDFs and multiple QTEs. To account for the observational nature of the data and ensure an efficient model, we adjust for a double balancing score that augments the propensity score with individual covariates. We provide a Bayesian estimation framework that appropriately propagates modeling uncertainty. We show via simulations that the use of double balancing score for confounding adjustment improves performance over adjusting for any single score alone, and the proposed semiparametric model estimates QTEs more accurately than other semiparametric methods. We apply the proposed method to the North Carolina birth weight dataset to analyze the effect of maternal smoking on infant's birth weight.Comment: 35 pages, 8 figure

A forensically-enabled IAAS cloud computing architecture

Current cloud architectures do not support digital forensic investigators, nor comply with today’s digital forensics procedures largely due to the dynamic nature of the cloud. Whilst much research has focused upon identifying the problems that are introduced with a cloud-based system, to date there is a significant lack of research on adapting current digital forensic tools and techniques to a cloud environment. Data acquisition is the first and most important process within digital forensics – to ensure data integrity and admissibility. However, access to data and the control of resources in the cloud is still very much provider-dependent and complicated by the very nature of the multi-tenanted operating environment. Thus, investigators have no option but to rely on cloud providers to acquire evidence, assuming they would be willing or are required to by law. Furthermore, the evidence collected by the Cloud Service Providers (CSPs) is still questionable as there is no way to verify the validity of this evidence and whether evidence has already been lost. This paper proposes a forensic acquisition and analysis model that fundamentally shifts responsibility of the data back to the data owner rather than relying upon a third party. In this manner, organisations are free to undertaken investigations at will requiring no intervention or cooperation from the cloud provider. The model aims to provide a richer and complete set of admissible evidence than what current CSPs are able to provide