@thesis{ahmadi_mehri_towards_2019,
	location = {Karlskrona},
	title = {Towards Secure Collaborative {AI} Service Chains},
	url = {http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Abth-18531},
	abstract = {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.},
	pagetotal = {146},
	institution = {Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.},
	type = {Licentiate Thesis},
	author = {Ahmadi Mehri, Vida},
	urldate = {2020-01-28},
	date = {2019},
}