Change search
Refine search result
1 - 7 of 7
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Bessani, A.
    et al.
    Brandt, J.
    Bux, M.
    Cogo, V.
    Dimitrova, L.
    Dowling, Jim
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Gholami, Ali
    KTH.
    Hakimzadeh, Kamal
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Hummel, M.
    Ismail, Mahmoud
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Laure, Erwin
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC. KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).
    Leser, U.
    Litton, J. -E
    Martinez, R.
    Niazi, Salman
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Reichel, J.
    Zimmermann, K.
    BiobankCloud: A platform for the secure storage, sharing, and processing of large biomedical data sets2016In: 1st International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2015 and Workshop on Big-Graphs Online Querying, Big-O(Q) 2015 held in conjunction with 41st International Conference on Very Large Data Bases, VLDB 2015, Springer, 2016, p. 89-105Conference paper (Refereed)
    Abstract [en]

    Biobanks store and catalog human biological material that is increasingly being digitized using next-generation sequencing (NGS). There is, however, a computational bottleneck, as existing software systems are not scalable and secure enough to store and process the incoming wave of genomic data from NGS machines. In the BiobankCloud project, we are building a Hadoop-based platform for the secure storage, sharing, and parallel processing of genomic data. We extended Hadoop to include support for multi-tenant studies, reduced storage requirements with erasure coding, and added support for extensible and consistent metadata. On top of Hadoop, we built a scalable scientific workflow engine featuring a proper workflow definition language focusing on simple integration and chaining of existing tools, adaptive scheduling on Apache Yarn, and support for iterative dataflows. Our platform also supports the secure sharing of data across different, distributed Hadoop clusters. The software is easily installed and comes with a user-friendly web interface for running, managing, and accessing data sets behind a secure 2-factor authentication. Initial tests have shown that the engine scales well to dozens of nodes. The entire system is open-source and includes pre-defined workflows for popular tasks in biomedical data analysis, such as variant identification, differential transcriptome analysis using RNA-Seq, and analysis of miRNA-Seq and ChIP-Seq data.

  • 2.
    Cameron, David
    et al.
    University of Oslo, Nordic Data Grid Facility, Kastrup, Danmark.
    Gholami, Ali
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Karpenko, Dmytro
    University of Oslo.
    Konstantinov, Aleksandr
    University of Oslo, Vilnius University Institute of Applied Research.
    Adaptive data management in the ARC Grid middleware2011In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 331, no 062006Article in journal (Refereed)
    Abstract [en]

    The Advanced Resource Connector (ARC) Grid middleware was designed almost 10 years ago, and has proven to be an attractive distributed computing solution and successful in adapting to new data management and storage technologies. However, with an ever-increasing user base and scale of resources to manage, along with the introduction of more advanced data transfer protocols, some limitations in the current architecture have become apparent. The simple first-in first-out approach to data transfer leads to bottlenecks in the system, as does the built-in assumption that all data is immediately available from remote data storage. We present an entirely new data management architecture for ARC which aims to alleviate these problems, by introducing a three-layer structure. The top layer accepts incoming requests for data transfer and directs them to the middle layer, which schedules individual transfers and negotiates with various intermediate catalog and storage systems until the physical file is ready to be transferred. The lower layer performs all operations which use large amounts of bandwidth, i.e. the physical data transfer. Using such a layered structure allows more efficient use of the available bandwidth as well as enabling late-binding of jobs to data transfer slots based on a priority system. Here we describe in full detail the design and implementation of the new system.

  • 3.
    Gholami, Ali
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Security and Privacy of Sensitive Data in Cloud Computing2016Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Cloud computing offers the prospect of on-demand, elastic computing, provided as a utility service, and it is revolutionizing many domains of computing. Compared with earlier methods of processing data, cloud computing environments provide significant benefits, such as the availability of automated tools to assemble, connect, configure and reconfigure virtualized resources on demand. These make it much easier to meet organizational goals as organizations can easily deploy cloud services. However, the shift in paradigm that accompanies the adoption of cloud computing is increasingly giving rise to security and privacy considerations relating to facets of cloud computing such as multi-tenancy, trust, loss of control and accountability. Consequently, cloud platforms that handle sensitive information are required to deploy technical measures and organizational safeguards to avoid data protection breakdowns that might result in enormous and costly damages. Sensitive information in the context of cloud computing encompasses data from a wide range of different areas and domains. Data concerning health is a typical example of the type of sensitive information handled in cloud computing environments, and it is obvious that most individuals will want information related to their health to be secure. Hence, with the growth of cloud computing in recent times, privacy and data protection requirements have been evolving to protect individuals against surveillance and data disclosure. Some examples of such protective legislation are the EU Data Protection Directive (DPD) and the US Health Insurance Portability and Accountability Act (HIPAA), both of which demand privacy preservation for handling personally identifiable information. There have been great efforts to employ a wide range of mechanisms to enhance the privacy of data and to make cloud platforms more secure. Techniques that have been used include: encryption, trusted platform module, secure multi-party computing, homomorphic encryption, anonymization, container and sandboxing technologies. However, it is still an open problem about how to correctly build usable privacy-preserving cloud systems to handle sensitive data securely due to two research challenges. First, existing privacy and data protection legislation demand strong security, transparency and audibility of data usage. Second, lack of familiarity with a broad range of emerging or existing security solutions to build efficient cloud systems. This dissertation focuses on the design and development of several systems and methodologies for handling sensitive data appropriately in cloud computing environments. The key idea behind the proposed solutions is enforcing the privacy requirements mandated by existing legislation that aims to protect the privacy of individuals in cloud-computing platforms. We begin with an overview of the main concepts from cloud computing, followed by identifying the problems that need to be solved for secure data management in cloud environments. It then continues with a description of background material in addition to reviewing existing security and privacy solutions that are being used in the area of cloud computing. Our first main contribution is a new method for modeling threats to privacy in cloud environments which can be used to identify privacy requirements in accordance with data protection legislation. This method is then used to propose a framework that meets the privacy requirements for handling data in the area of genomics. That is, health data concerning the genome (DNA) of individuals. Our second contribution is a system for preserving privacy when publishing sample availability data. This system is noteworthy because it is capable of cross-linking over multiple datasets. The thesis continues by proposing a system called ScaBIA for privacy-preserving brain image analysis in the cloud. The final section of the dissertation describes a new approach for quantifying and minimizing the risk of operating system kernel exploitation, in addition to the development of a system call interposition reference monitor for Lind - a dual sandbox.

  • 4.
    Gholami, Ali
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Dowling, Jim
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Laure, Erwin
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC. KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    A security framework for population-scale genomics analysis2015In: Proceedings of the 2015 International Conference on High Performance Computing and Simulation, HPCS 2015, IEEE conference proceedings, 2015, p. 106-114Conference paper (Refereed)
    Abstract [en]

    Biobanks store genomic material from identifiable individuals. Recently many population-based studies have started sequencing genomic data from biobank samples and cross-linking the genomic data with clinical data, with the goal of discovering new insights into disease and clinical treatments. However, the use of genomic data for research has far-reaching implications for privacy and the relations between individuals and society. In some jurisdictions, primarily in Europe, new laws are being or have been introduced to legislate for the protection of sensitive data relating to individuals, and biobank-specific laws have even been designed to legislate for the handling of genomic data and the clear definition of roles and responsibilities for the owners and processors of genomic data. This paper considers the security questions raised by these developments. We introduce a new threat model that enables the design of cloud-based systems for handling genomic data according to privacy legislation. We also describe the design and implementation of a security framework using our threat model for BiobankCloud, a platform that supports the secure storage and processing of genomic data in cloud computing environments.

  • 5.
    Gholami, Ali
    et al.
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).
    Lind, Anna-Sara
    Reichel, Jane
    Litton, Jan-Eric
    Edlund, Åke
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).
    Laure, Erwin
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).
    Privacy Threat Modeling for Emerging BiobankClouds2014In: Procedia Computer Science: The 5th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2014)/ The 4th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH 2014)/ Affiliated Workshops, Elsevier, 2014, Vol. 37, p. 489-496Conference paper (Refereed)
    Abstract [en]

    There is an increased amount of data produced by next generation sequencing (NGS) machines which demand scalable storage and analysis of genomic data. In order to cope with this huge amount of information, many biobanks are interested in cloud computing capabilities such as on-demand elasticity of computing power and storage capacity. There are several security and privacy requirements mandated by personal data protection legislation which hinder biobanks from migrating big data generated by the NGS machines. This paper describes the privacy requirements of platform-as-service BiobankClouds according to the European Data Protection Directive (DPD). It identifies several key privacy threats which leave BiobankClouds vulnerable to an attack. This study benefits health-care application designers in the requirement elicitation cycle when building privacy-preserving BiobankCloud platforms.

  • 6.
    Gholami, Ali
    et al.
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Svensson, Gert
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Laure, Erwin
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Eickhoff, M.
    Brasche, G.
    ScaBIA: Scalable brain image analysis in the cloud2013In: CLOSER 2013 - Proceedings of the 3rd International Conference on Cloud Computing and Services Science, 2013, p. 329-336Conference paper (Refereed)
    Abstract [en]

    The use of cloud computing as a new paradigm has become a reality. Cloud computing leverages the use of on-demand CPU power and storage resources while eliminating the cost of commodity hardware ownership. Cloud computing is now gaining popularity among many different organizations and commercial sectors. In this paper, we present the scalable brain image analysis (ScaBIA) architecture, a new model to run statistical parametric analysis (SPM) jobs using cloud computing. SPM is one of the most popular toolkits in neuroscience for running compute-intensive brain image analysis tasks. However, issues such as sharing raw data and results, as well as scalability and performance are major bottlenecks in the "single PC"-execution model. In this work, we describe a prototype using the generic worker (GW), an e-Science as a service middleware, on top of Microsoft Azure to run and manage the SPM tasks. The functional prototype shows that ScaBIA provides a scalable framework for multi-job submission and enables users to share data securely using storage access keys across different organizations.

  • 7.
    Hedman, Fredrik
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Riedel, Morris
    Jülich Supercomputing Centre, Forschungszentrum Jülich.
    Mucci, Phillip
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Netzer, Gilbert
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Gholami, Ali
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Memon, M. Shahbaz
    Jülich Supercomputing Centre, Forschungszentrum Jülich.
    Memon, A. Shiraz
    Jülich Supercomputing Centre, Forschungszentrum Jülich.
    Shah, Zeeshan A.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    Benchmarking of Integrated OGSA-BES with the Grid Middleware2009In: EURO-PAR 2008 WORKSHOPS - PARALLEL PROCESSING / [ed] Eduardo César, Michael Alexander, Achim Streit, Jesper Larsson Träff, Christophe Cérin, Andreas Knüpfer, Dieter Kranzelmüller, Shantenu Jha, Berlin: Springer Berlin/Heidelberg, 2009, p. 113-122Conference paper (Refereed)
    Abstract [en]

    This paper evaluates the performance of the emerging OGF standard OGSA - Basic Execution Service (BES) on three fundamentally different Grid middleware platforms: UNICORE 5/6, Globus Toolkit 4 and gLite. The particular focus within this paper is on the OGSA-BES implementation of UNICORE 6. A comparison is made with baseline measurements, for UNICORE 6 and Globus Toolkit 4, using the legacy job submission interfaces. Our results show that the BES components are comparable in performance to existing legacy interfaces. We also have a strong indication that other factors, attributable to the supporting infrastructure, have a bigger impact on performance than BES components.

1 - 7 of 7
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf