Better Safe than Sorry: A Study of Investigating Individuals’ Protection of Privacy in the Use of Storage as a Cloud Computing Service

Since the use of network-based storage as a cloud computing service [Storage as a Service (StaaS)] becomes ubiquitous, users who store data in the cloud are exposed to risk and potentially severe consequences. Therefore, individuals should carefully consider security and privacy issues when using StaaS. Drawing upon the principle of self-preservation and the theory of protection motivation, this study develops a research model explaining the mechanisms leading to the development of a protection strategy for individuals’ safe use of StaaS and empirically tests the proposed model. The results show that the natural propensity to trust, an individual trait, influences an individual’s need for privacy. This, in turn, along with perception of privacy risk, significantly affects individuals’ perception of the need to protect themselves. Three coping appraisal factors, preventive measures, self-efficacy, and willingness to incur cost of prevention, together with the individual perceived need to protect self, directly and positively affect users’ need to develop a protection strategy in a cloud computing environment. Theoretical and practical implications are discussed.     

Bitwise dimensional co-clustering for analytical workloads

Analytical workloads in data warehouses often include heavy joins where queries involve multiple fact tables in addition to the typical star-patterns, dimensional grouping and selections. In this paper we propose a new processing and storage framework called bitwise dimensional co-clustering (BDCC) that avoids replication and thus keeps updates fast, yet is able to accelerate all these foreign key joins, efficiently support grouping and pushes down most dimensional selections. The core idea of BDCC is to cluster each table on a mix of dimensions, each possibly derived from attributes imported over an incoming foreign key and this way creating foreign key connected tables with partially shared clusterings. These are later used to accelerate any join between two tables that have some dimension in common and additionally permit to push down and propagate selections (reduce I/O) and accelerate aggregation and ordering operations. Besides the general framework, we describe an algorithm to derive such a physical co-clustering database automatically and describe query processing and query optimization techniques that can easily be fitted into existing relational engines. We present an experimental evaluation on the TPC-H benchmark in the Vectorwise system, showing that co-clustering can significantly enhance its already high performance and at the same time significantly reduce the memory consumption of the system.     

An empirical study of integration activities in distributions of open source software

Reuse of software components, either closed or open source, is considered to be one of the most important best practices in software engineering, since it reduces development cost and improves software quality. However, since reused components are (by definition) generic, they need to be customized and integrated into a specific system before they can be useful. Since this integration is system-specific, the integration effort is non-negligible and increases maintenance costs, especially if more than one component needs to be integrated. This paper performs an empirical study of multi-component integration in the context of three successful open source distributions (Debian, Ubuntu and FreeBSD). Such distributions integrate thousands of open source components with an operating system kernel to deliver a coherent software product to millions of users worldwide. We empirically identified seven major integration activities performed by the maintainers of these distributions, documented how these activities are being performed by the maintainers, then evaluated and refined the identified activities with input from six maintainers of the three studied distributions. The documented activities provide a common vocabulary for component integration in open source distributions and outline a roadmap for future research on software integration.     

Service-Oriented Security Architecture and its Implications for Security Department Organization Structures

ABSTRACT

Cyber-crime and malware has evolved so drastically over the last two years that it is a challenge for an IT professional to stay on top of recent malware trends and technological advances in cyber-security. This paper provides a look from inside the antivirus laboratory at current malware attacks and technology developments for effective defenses.     

Bed morphology, flow structure, and sediment transport at the outlet of Lake Huron and in the upper St. Clair River

An integrated multibeam echo sounder and acoustic Doppler current profiler field survey was conducted in July 2008 to investigate the morphodynamics of the St. Clair River at the outlet of Lake Huron. The principal morphological features of the upper St. Clair River included flow-transverse bedforms that appear weakly mobile, erosive bedforms in cohesive muds, thin non-cohesive veneers of weakly mobile sediment that cover an underlying cohesive (till or glacio-lacustrine) surface, and vegetation that covers the bed. The flow was characterized by acceleration as the banks constrict from Lake Huron into the St. Clair River, an approximately 1500-m long region of flow separation downstream from the Blue Water Bridge, and secondary flow connected to: i) channel curvature; ii) forcing of the flow by local bed topography, and iii) flow wakes in the lee side of ship wrecks. Nearshore, sand-sized, sediment from Lake Huron was capable of being transported into, and principally along, the banks of the upper St. Clair River by the measured flow. A comparison of bathymetric surveys conducted in 2007 and 2008 identifies that the gravel bed does undergo slow downstream movement, but that this movement does not appear to be generated by the mean flow, and could possibly be caused by ship-propeller-induced turbulence. The study results suggest that the measured mean flow and dredging within the channel have not produced major scour of the upper St. Clair River and that the recent fall in the level of Lake Huron is unlikely to have been caused by these mechanisms.     

Performance models of storage contention in cloud environments

We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found our models to be effective in predicting such quantities across a range of synthetic and emulated application workloads.     

Engineering the Irish Software Tiger

Information and communication technologies, particularly software, play a crucial role in the Republic of Ireland's remarkable economic growth. Successful globalization has posed many challenges and fostered a major strategic investment in research. Ireland's unique position has a major influence on the realization of the software engineering research agenda.     

Simultaneous nonrigid registration of multiple point sets and atlas construction

Group-wise registration of a set of shapes represented by unlabeled point-sets is a challenging problem since, usually this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape represented by a probability density function from multiple unlabeled point-sets and registering them non-rigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. We derive the analytic gradient of the cost function in order to efficiently achieve the optimal solution. JS-divergence is symmetric with no bias toward any of the given shapes to be registered and whose mean is being sought. A by product of the registration process is a probabilistic atlas defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images as well as for simultaneously (rigidly or non-rigidly) registering 3D range data sets without having to establish any correspondence. We present experimental results on real and synthetic data.     

Inductive transfer with context-sensitive neural networks

Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer which uses a single output neural network and additional contextual inputs for learning multiple tasks. Motivated by problems with the application of MTL networks to machine lifelong learning systems, csMTL encoding of multiple task examples was developed and found to improve predictive performance. As evidence, the csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination of IDT and SVM models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning models.