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Privacy and DRM Requirements for Collaborative Development of AI Application
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of technology. (Cloud, Networking and Security)ORCID iD: 0000-0002-0128-4127
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. (Cloud, Networking and Security)ORCID iD: 0000-0001-8453-447X
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. (Cloud, Networking and Security)ORCID iD: 0000-0003-4814-4428
2018 (English)In: ACM International Conference Proceeding Series, 2018, article id 3233268Conference paper, Published 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. 

Place, publisher, year, edition, pages
2018. article id 3233268
Keywords [en]
Privacy, DRM, AI, collaborative
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:bth-16867DOI: 10.1145/3230833.3233268ISBN: 978-1-4503-6448-5 (print)OAI: oai:DiVA.org:bth-16867DiVA, id: diva2:1238658
Conference
13th International Conference on Availability, Reliability and Security, ARES 2018; Hamburg; Germany; 27 August 2018 through 30 August
Projects
H2020 Bonseyes
Funder
EU, Horizon 2020, 732204Available from: 2018-08-14 Created: 2018-08-14 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, Karlskrona, 00:00 (English)
Opponent
Supervisors
Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2019-08-09Bibliographically approved

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Ahmadi Mehri, VidaIlie, DragosTutschku, Kurt
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Citation style
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