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Flexible Privacy and High Trust in the Next Generation Internet: The Use Case of a Cloud-based Marketplace for AI
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-0128-4127
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-4814-4428
2017 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
Halmstad university , 2017.
Keywords [en]
marketplace, privacy, trust, cloud computing
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:bth-14963OAI: oai:DiVA.org:bth-14963DiVA, id: diva2:1128475
Conference
SNCNW - Swedish National Computer Networking Workshop, Halmstad
Projects
Horizon 2020 Bonseyes
Funder
EU, Horizon 2020, 732204Available from: 2017-07-25 Created: 2017-07-25 Last updated: 2019-08-09Bibliographically approved
In thesis
1. Towards Secure Collaborative AI Service Chains
Open this publication in new window or tab >>Towards Secure Collaborative AI Service Chains
2019 (English)Licentiate 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.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019. p. 146
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 11
National Category
Telecommunications
Identifiers
urn:nbn:se:bth-18531 (URN)978-91-7295-381-9 (ISBN)
Presentation
2019-09-10, J1620, Campus Gräsvik, Karlskrona, 12:30 (English)
Opponent
Supervisors
Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2019-09-03Bibliographically approved

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Mehri, Vida. A.Tutschku, Kurt

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