Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Privacy and DRM Requirements for Collaborative Development of AI Application
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik. Blekinge Institute of technology. (Cloud, Networking and Security)ORCID-id: 0000-0002-0128-4127
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik. (Cloud, Networking and Security)ORCID-id: 0000-0001-8453-447X
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik. (Cloud, Networking and Security)ORCID-id: 0000-0003-4814-4428
2018 (Engelska)Ingår i: ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2018, artikel-id 3233268Konferensbidrag, Publicerat paper (Refereegranskat)
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. 

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery (ACM), 2018. artikel-id 3233268
Nyckelord [en]
Privacy, DRM, AI, collaborative
Nationell ämneskategori
Telekommunikation
Identifikatorer
URN: urn:nbn:se:bth-16867DOI: 10.1145/3230833.3233268ISI: 000477981800053ISBN: 978-1-4503-6448-5 (tryckt)OAI: oai:DiVA.org:bth-16867DiVA, id: diva2:1238658
Konferens
13th International Conference on Availability, Reliability and Security, ARES; Hamburg; Germany; 27 August 2018 through 30 August
Projekt
H2020 Bonseyes
Forskningsfinansiär
EU, Horisont 2020, 732204Tillgänglig från: 2018-08-14 Skapad: 2018-08-14 Senast uppdaterad: 2019-09-10Bibliografiskt granskad
Ingår i avhandling
1. Towards Secure Collaborative AI Service Chains
Öppna denna publikation i ny flik eller fönster >>Towards Secure Collaborative AI Service Chains
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Karlskrona: Blekinge Tekniska Högskola, 2019. s. 146
Serie
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 11
Nationell ämneskategori
Telekommunikation
Identifikatorer
urn:nbn:se:bth-18531 (URN)978-91-7295-381-9 (ISBN)
Presentation
2019-09-10, J1620, Campus Gräsvik, Karlskrona, 12:30 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-08-09 Skapad: 2019-08-09 Senast uppdaterad: 2019-09-03Bibliografiskt granskad

Open Access i DiVA

fulltext(1854 kB)95 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 1854 kBChecksumma SHA-512
87384e66a61d873f841293c4c28a33be549836c4dcf3b4ccd01efb11fbc02e30362c09efef9121521300ade38d062fb3d17af2b27a2600abb5701e4b440650fe
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltext

Sök vidare i DiVA

Av författaren/redaktören
Ahmadi Mehri, VidaIlie, DragosTutschku, Kurt
Av organisationen
Institutionen för datalogi och datorsystemteknik
Telekommunikation

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 95 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 774 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf