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Enhancing Privacy in the Advanced Metering Infrastructure: Efficient Methods, the Role of Data Characteristics and Applications

Valentin Tudor (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) )
Gothenburg : Chalmers University of Technology, 2017. ISBN: 978-91-7597-632-7.

Large quantities of data are produced and collected by computing and communication devices in cyber-physical systems. Information extracted from these data opens new possibilities but also raises privacy issues.

The characteristics of these data play an important role in the efficiency of privacy-enhancing technologies thus grasping the former's influence is a step forward in improving the latter. Privacy-enhanced data can be employed in cyber-physical systems' applications and their utility can be improved by fine-tuning the parameters of the privacy-enhancing technologies applied to the data. This can be coupled with an analysis of the efficiency of applications that employ privacy-enhanced preprocessed data for better insights on the trade-off between applications' utility and data privacy. Orthogonal to this, privacy-enhanced data originating from cyber-physical systems can be employed in monitoring solutions for cyber security. This is a step forward in fulfilling both the confidentiality and privacy requirements for these complex systems.

This thesis focuses on privacy in the context of the Advanced Metering Infrastructure (AMI) in the smart electrical grid and it has three primary objectives. The first is to study the characteristics of AMI datasets and how they influence the efficiency of privacy enhancing technologies. The second objective is to identify methods and efficient algorithmic implementations, in connection to what can be deployed in contemporary hardware, as needed for Internet of Things-based systems. The third objective is to study the balance between confidentiality requirements and the requirement to monitor the communication network for intrusion detection, as an example.

This thesis advances the current research by showing (i) how different AMI privacy-enhancing techniques complement each other, (ii) how datasets' characteristics can be tuned in order to improve the efficiency of these techniques and (iii) how the need for privacy can be balanced with the need to monitor the AMI communication network.

Nyckelord: applied differential privacy, data characteristics, intrusion detection, data privacy, Advanced Metering Infrastructure, communication security, system security

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Denna post skapades 2017-09-07. Senast ändrad 2017-09-18.
CPL Pubid: 251765