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University of Arkansas

1. Nugroho, Amin Rois Sinung. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.

Degree: MS, 2016, University of Arkansas

Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the usage information of mobile apps (e.g., how many hours a user plays mobile games in each day), private information about a user’s living habits and personal activities can be inferred. To assess this risk, this thesis answers the following research question: can the type of a mobile app (e.g., email, web browsing, mobile game, music streaming, etc.) used by a user be inferred from the resource (e.g., CPU, memory, network, etc.) usage patterns of the mobile app? This thesis answers this question for two kinds of systems, a single mobile device and a mobile cloud computing system. First, two privacy attacks under the same framework are proposed based on supervised learning algorithms. Then these attacks are implemented and explored in a mobile device and in a cloud computing environment. Experimental evaluations show that the type of app can be inferred with high probability. In particular, the attacks achieve up to 100% accuracy on a mobile device, and 66.7% accuracy in the mobile cloud computing environment. This study shows that resource usage patterns of mobile apps can be used to infer the type of apps being used, and thus can cause privacy leakage if not protected. Advisors/Committee Members: Qinghua Li, Xintao Wu, Tingxin Yan.

Subjects/Keywords: Applied sciences; CPU usage; Cloud; Machine learning; Mobile; Mobile app; Privacy; Information Security

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Nugroho, A. R. S. (2016). Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1599

Chicago Manual of Style (16th Edition):

Nugroho, Amin Rois Sinung. “Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.” 2016. Masters Thesis, University of Arkansas. Accessed October 24, 2020. https://scholarworks.uark.edu/etd/1599.

MLA Handbook (7th Edition):

Nugroho, Amin Rois Sinung. “Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps.” 2016. Web. 24 Oct 2020.

Vancouver:

Nugroho ARS. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. [Internet] [Masters thesis]. University of Arkansas; 2016. [cited 2020 Oct 24]. Available from: https://scholarworks.uark.edu/etd/1599.

Council of Science Editors:

Nugroho ARS. Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps. [Masters Thesis]. University of Arkansas; 2016. Available from: https://scholarworks.uark.edu/etd/1599

2. Paulo Antonio Leal Rego. FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento.

Degree: Master, 2012, Universidade Federal do Ceará

O escalonamento de recursos à um processo chave para a plataforma de ComputaÃÃo em Nuvem, que geralmente utiliza mÃquinas virtuais (MVs) como unidades de escalonamento. O uso de tÃcnicas de virtualizaÃÃo fornece grande flexibilidade com a habilidade de instanciar vÃrias MVs em uma mesma mÃquina fÃsica (MF), modificar a capacidade das MVs e migrÃ-las entre as MFs. As tÃcnicas de consolidaÃÃo e alocaÃÃo dinÃmica de MVs tÃm tratado o impacto da sua utilizaÃÃo como uma medida independente de localizaÃÃo. à geralmente aceito que o desempenho de uma MV serà o mesmo, independentemente da MF em que ela à alocada. Esta à uma suposiÃÃo razoÃvel para um ambiente homogÃneo, onde as MFs sÃo idÃnticas e as MVs estÃo executando o mesmo sistema operacional e aplicativos. No entanto, em um ambiente de ComputaÃÃo em Nuvem, espera-se compartilhar um conjunto composto por recursos heterogÃneos, onde as MFs podem variar em termos de capacidades de seus recursos e afinidades de dados. O objetivo principal deste trabalho à apresentar uma arquitetura que possibilite a padronizaÃÃo da representaÃÃo do poder de processamento das MFs e MVs, em funÃÃo de Unidades de Processamento (UPs), apoiando-se na limitaÃÃo do uso da CPU para prover isolamento de desempenho e manter a capacidade de processamento das MVs independente da MF subjacente. Este trabalho busca suprir a necessidade de uma soluÃÃo que considere a heterogeneidade das MFs presentes na infraestrutura da Nuvem e apresenta polÃticas de escalonamento baseadas na utilizaÃÃo das UPs. A arquitetura proposta, chamada FairCPU, foi implementada para trabalhar com os hipervisores KVM e Xen, e foi incorporada a uma nuvem privada, construÃda com o middleware OpenNebula, onde diversos experimentos foram realizados para avaliar a soluÃÃo proposta. Os resultados comprovam a eficiÃncia da arquitetura FairCPU em utilizar as UPs para reduzir a variabilidade no desempenho das MVs, bem como para prover uma nova maneira de representar e gerenciar o poder de processamento das MVs e MFs da infraestrutura.

Resource scheduling is a key process for cloud computing platform, which generally uses virtual machines (VMs) as scheduling units. The use of virtualization techniques provides great flexibility with the ability to instantiate multiple VMs on one physical machine (PM), migrate them between the PMs and dynamically scale VMâs resources. The techniques of consolidation and dynamic allocation of VMs have addressed the impact of its use as an independent measure of location. It is generally accepted that the performance of a VM will be the same regardless of which PM it is allocated. This assumption is reasonable for a homogeneous environment where the PMs are identical and the VMs are running the same operating system and applications. Nevertheless, in a cloud computing environment, we expect that a set of heterogeneous resources will be shared, where PMs will face changes both in terms of their resource capacities and as also in data affinities. The main objective of this work is to propose an architecture…

Advisors/Committee Members: Miguel Franklin de Castro, Josà Neuman de Souza, Bruno Richard Schulze, Paulo Henrique Mendes Maia.

Subjects/Keywords: CIENCIA DA COMPUTACAO; Provisionamento de mÃquinas virtuais; Ambiente heterogÃneo; LimitaÃÃo do uso da CPU; ComputaÃÃo em nuvem; VirtualizaÃÃo; Escalonamento; Unidade de processamento; Variabilidade de desempenho; KVM; Xen; OpenNebula; Virtual machines provisioning; Heterogeneous environment; Limit CPU usage; Cloud computing; Virtualization: Scheduling; Processing unit; Performance variability; KVM; Xen; OpenNebula; ComputaÃÃo em nuvem; Processamento eletrÃnico de dados - Processamento distribuido; MÃquinas leitoras (Processamento de dados)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rego, P. A. L. (2012). FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento. (Masters Thesis). Universidade Federal do Ceará. Retrieved from http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7653 ;

Chicago Manual of Style (16th Edition):

Rego, Paulo Antonio Leal. “FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento.” 2012. Masters Thesis, Universidade Federal do Ceará. Accessed October 24, 2020. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7653 ;.

MLA Handbook (7th Edition):

Rego, Paulo Antonio Leal. “FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento.” 2012. Web. 24 Oct 2020.

Vancouver:

Rego PAL. FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento. [Internet] [Masters thesis]. Universidade Federal do Ceará 2012. [cited 2020 Oct 24]. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7653 ;.

Council of Science Editors:

Rego PAL. FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento. [Masters Thesis]. Universidade Federal do Ceará 2012. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7653 ;

3. Sivakumar, Aswin. Geometry Aware Compressive Analysis of Human Activities : Application in a Smart Phone Platform.

Degree: MS, Electrical Engineering, 2014, Arizona State University

Continuous monitoring of sensor data from smart phones to identify human activities and gestures, puts a heavy load on the smart phone's power consumption. In this research study, the non-Euclidean geometry of the rich sensor data obtained from the user's smart phone is utilized to perform compressive analysis and efficient classification of human activities by employing machine learning techniques. We are interested in the generalization of classical tools for signal approximation to newer spaces, such as rotation data, which is best studied in a non-Euclidean setting, and its application to activity analysis. Attributing to the non-linear nature of the rotation data space, which involve a heavy overload on the smart phone's processor and memory as opposed to feature extraction on the Euclidean space, indexing and compaction of the acquired sensor data is performed prior to feature extraction, to reduce CPU overhead and thereby increase the lifetime of the battery with a little loss in recognition accuracy of the activities. The sensor data represented as unit quaternions, is a more intrinsic representation of the orientation of smart phone compared to Euler angles (which suffers from Gimbal lock problem) or the computationally intensive rotation matrices. Classification algorithms are employed to classify these manifold sequences in the non-Euclidean space. By performing customized indexing (using K-means algorithm) of the evolved manifold sequences before feature extraction, considerable energy savings is achieved in terms of smart phone's battery life.

Subjects/Keywords: Electrical engineering; CPU Usage; Human Activity Recognition; Non Euclidean Geometry; Symbolic Representation; Unit Quaternions; Unit sphere- S-3 manifold

CPU usage. For benchmarking the CPU usage, studies are carried out by comparing with text to… …computation and thus increased CPU Usage (due to frequent PAA operation in SAX). This is… …23 3.10 Effect on SAX on CPU Computational Time… …24 3.12 Effects of varying Approximation parameters on CPU Computational Time… …40 5.2 Battery Usage: PAA Window Size variation… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sivakumar, A. (2014). Geometry Aware Compressive Analysis of Human Activities : Application in a Smart Phone Platform. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/25156

Chicago Manual of Style (16th Edition):

Sivakumar, Aswin. “Geometry Aware Compressive Analysis of Human Activities : Application in a Smart Phone Platform.” 2014. Masters Thesis, Arizona State University. Accessed October 24, 2020. http://repository.asu.edu/items/25156.

MLA Handbook (7th Edition):

Sivakumar, Aswin. “Geometry Aware Compressive Analysis of Human Activities : Application in a Smart Phone Platform.” 2014. Web. 24 Oct 2020.

Vancouver:

Sivakumar A. Geometry Aware Compressive Analysis of Human Activities : Application in a Smart Phone Platform. [Internet] [Masters thesis]. Arizona State University; 2014. [cited 2020 Oct 24]. Available from: http://repository.asu.edu/items/25156.

Council of Science Editors:

Sivakumar A. Geometry Aware Compressive Analysis of Human Activities : Application in a Smart Phone Platform. [Masters Thesis]. Arizona State University; 2014. Available from: http://repository.asu.edu/items/25156

.