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  • 1.
    Ahmadi Mehri, Vida
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Towards Secure Collaborative AI Service Chains2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    At present, Artificial Intelligence (AI) systems have been adopted in many different domains such as healthcare, robotics, automotive, telecommunication systems, security, and finance for integrating intelligence in their services and applications. The intelligent personal assistant such as Siri and Alexa are examples of AI systems making an impact on our daily lives. Since many AI systems are data-driven systems, they require large volumes of data for training and validation, advanced algorithms, computing power and storage in their development process. Collaboration in the AI development process (AI engineering process) will reduce cost and time for the AI applications in the market. However, collaboration introduces the concern of privacy and piracy of intellectual properties, which can be caused by the actors who collaborate in the engineering process.  This work investigates the non-functional requirements, such as privacy and security, for enabling collaboration in AI service chains. It proposes an architectural design approach for collaborative AI engineering and explores the concept of the pipeline (service chain) for chaining AI functions. In order to enable controlled collaboration between AI artefacts in a pipeline, this work makes use of virtualisation technology to define and implement Virtual Premises (VPs), which act as protection wrappers for AI pipelines. A VP is a virtual policy enforcement point for a pipeline and requires access permission and authenticity for each element in a pipeline before the pipeline can be used.  Furthermore, the proposed architecture is evaluated in use-case approach that enables quick detection of design flaw during the initial stage of implementation. To evaluate the security level and compliance with security requirements, threat modeling was used to identify potential threats and vulnerabilities of the system and analyses their possible effects. The output of threat modeling was used to define countermeasure to threats related to unauthorised access and execution of AI artefacts.

  • 2.
    Ahmadi Mehri, Vida
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of technology.
    Ilie, Dragos
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tutschku, Kurt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Privacy and DRM Requirements for Collaborative Development of AI Application2018In: ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2018, article id 3233268Conference paper (Refereed)
    Abstract [en]

    The use of data is essential for the capabilities of Data-driven Artificial intelligence (AI), Deep Learning and Big Data analysis techniques. This data usage, however, raises intrinsically the concerns on data privacy. In addition, supporting collaborative development of AI applications across organisations has become a major need in AI system design. Digital Rights Management (DRM) is required to protect intellectual property in such collaboration. As a consequence of DRM, privacy threats and privacy-enforcing mechanisms will interact with each other.

    This paper describes the privacy and DRM requirements in collaborative AI system design using AI pipelines. It describes the relationships between DRM and privacy and outlines the threats against these non-functional features. Finally, the paper provides first security architecture to protect against the threats on DRM and privacy in collaborative AI design using AI pipelines. 

  • 3.
    Ahmadi Mehri, Vida
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Ilie, Dragos
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tutschku, Kurt
    Towards Privacy Requirements for Collaborative Development of AI Applications2018In: 14th Swedish National Computer Networking Workshop (SNCNW), 2018Conference paper (Refereed)
    Abstract [en]

    The use of data is essential for the capabilities of Data- driven Artificial intelligence (AI), Deep Learning and Big Data analysis techniques. The use of data, however, raises intrinsically the concern of the data privacy, in particular for the individuals that provide data. Hence, data privacy is considered as one of the main non-functional features of the Next Generation Internet. This paper describes the privacy challenges and requirements for collaborative AI application development. We investigate the constraints of using digital right management for supporting collaboration to address the privacy requirements in the regulation.

  • 4.
    Mehri, Vida. A.
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Ilie, Dragos
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tutschku, Kurt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Designing a Secure IoT System Architecture from a Virtual Premise for a Collaborative AI Lab2019Conference paper (Refereed)
    Abstract [en]

    IoT systems are increasingly composed out of flexible, programmable, virtualised, and arbitrarily chained IoT elements and services using portable code. Moreover, they might be sliced, i.e. allowing multiple logical IoT systems (network + application) to run on top of a shared physical network and compute infrastructure. However, implementing and designing particularly security mechanisms for such IoT systems is challenging since a) promising technologies are still maturing, and b) the relationships among the many requirements, technologies and components are difficult to model a-priori.

    The aim of the paper is to define design cues for the security architecture and mechanisms of future, virtualised, arbitrarily chained, and eventually sliced IoT systems. Hereby, our focus is laid on the authorisation and authentication of user, host, and code integrity in these virtualised systems. The design cues are derived from the design and implementation of a secure virtual environment for distributed and collaborative AI system engineering using so called AI pipelines. The pipelines apply chained virtual elements and services and facilitate the slicing of the system. The virtual environment is denoted for short as the virtual premise (VP). The use-case of the VP for AI design provides insight into the complex interactions in the architecture, leading us to believe that the VP concept can be generalised to the IoT systems mentioned above. In addition, the use-case permits to derive, implement, and test solutions. This paper describes the flexible architecture of the VP and the design and implementation of access and execution control in virtual and containerised environments. 

  • 5.
    Mehri, Vida. A.
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tutschku, Kurt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Flexible Privacy and High Trust in the Next Generation Internet: The Use Case of a Cloud-based Marketplace for AI2017Conference paper (Refereed)
    Abstract [en]

    Cloudified architectures facilitate resource ac-cess and sharing which is independent from physical lo-cations. They permit high availability of resources at lowoperational costs. These advantages, however, do not comefor free. End users might fear that they lose control overthe location of their data and, thus, of their autonomy indeciding to whom the data is communicate to. Thus, strongprivacy and trust concerns arise for end users.In this work we will review and investigate privacy andtrust requirements for Cloud systems in general and for acloud-based marketplace (CMP) for AI in particular. We willinvestigate whether and how the current privacy and trustdimensions can be applied to Clouds and for the design ofa CMP. We also propose the concept of a "virtual premise"for enabling "Privacy-by-Design" [1] in Clouds. The ideaof a "virtual premise" might probably not be a universalsolution for any privacy requirement. However, we expectthat it provides flexibility in designing privacy in Cloudsand thus leading to higher trust.

  • 6.
    Mehri, Vida. A.
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tutschku, Kurt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Privacy and trust in cloud-based marketplaces for AI and data resources2017In: IFIP Advances in Information and Communication Technology, Springer New York LLC , 2017, Vol. 505, p. 223-225Conference paper (Refereed)
    Abstract [en]

    The processing of the huge amounts of information from the Internet of Things (IoT) has become challenging. Artificial Intelligence (AI) techniques have been developed to handle this task efficiently. However, they require annotated data sets for training, while manual preprocessing of the data sets is costly. The H2020 project “Bonseyes” has suggested a “Market Place for AI”, where the stakeholders can engage trustfully in business around AI resources and data sets. The MP permits trading of resources that have high privacy requirements (e.g. data sets containing patient medical information) as well as ones with low requirements (e.g. fuel consumption of cars) for the sake of its generality. In this abstract we review trust and privacy definitions and provide a first requirement analysis for them with regards to Cloud-based Market Places (CMPs). The comparison of definitions and requirements allows for the identification of the research gap that will be addressed by the main authors PhD project. © IFIP International Federation for Information Processing 2017.

  • 7.
    Tutschku, Kurt
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Ahmadi Mehri, Vida
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Carlsson, Anders
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Chivukula, Krishna Varaynya
    CityNetwork Webbhotell AB.
    Johan, Christenson
    CityNetwork Webbhotell AB.
    On Resource Description Capabilities of On-Board Tools for Resource Management in Cloud Networking and NFV Infrastructures2016In: 2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC), 2016, p. 442-447Conference paper (Refereed)
    Abstract [en]

    The rapid adoption of networks that are based on "cloudification" and Network Function Virtualisation (NFV) comes from the anticipated high cost savings of up to 70% in their build and operation. The high savings are founded in the use of general standard servers, instead of single-purpose hardware, and by efficiency resource sharing through virtualisation concepts. In this paper, we discuss the capabilities of resource description of "on-board" tools, i.e. using standard Linux commands, to enable OPEX savings. We put a focus on monitoring resources on small time-scales and on the variation observed on such scales. We introduce a QoE-based comparative concept that relates guest and host views on "utilisation" and "load" for the analysis of the variations. We describe the order of variations in "utilisation" and "load" by measurement and by graphical analysis of the measurements. We do these evaluations for different host operating systems and monitoring tools.

  • 8.
    Tutschku, Kurt Tutschku
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Ahmadi Mehri, Vida
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Carlsson, Anders
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Towards Multi-layer Resource Management in Cloud Networking and NFV Infrastructures2016Conference paper (Refereed)
    Abstract [en]

    Cloud Networking (CN) and related conceptsoffer appealing novelties to Cloud Computing (CC) customers.They can do a one-stop-shopping for network-enhanced cloudservices. In addition, the costs of such services might below due to multiple customers sharing the infrastructures.Moreover, telecommunication network operators are adopt-ing the CN in theirNetwork Functions Virtualisation (NFV)framework for reducing costs and increasing the flexibility oftheir networks. The technical appeal of CN comes from thetight integration of CC and smart networks. The economicalattractiveness results from avoiding dedicated hardware, shar-ing of resources, and simplified resource management (RM) asseen by the users respectively by the applications. The visionof cheap and integrated CN services is obviously attractive,but it is also evident that it will require more complex RMprocedures for efficiently balancing the usage of all resources.In this contribution, we suggest an initial architecture forintegrated and practical RM in CN and NFV systems. TheRM concept aims at locating and analysing performancebottlenecks, efficiency problems, and eventually discover un-used resources. The suggested architecture is based on alayered view on the system. Moreover, we detail difficultiesin practical resources usage monitoring which, in turn, definerequirements for a RM architecture. The requirement analysisis based on measurements in a CN infrastructure.

1 - 8 of 8
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