Enhancing Privacy in the Advanced Metering Infrastructure: Efficient Methods, the Role of Data Characteristics and Applications
Doctoral thesis, 2017

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.

intrusion detection

applied differential privacy

data privacy

Advanced Metering Infrastructure

communication security

data characteristics

system security

Room EC, EDIT Building
Opponent: Simone Fischer-Hübner, University of Karlstad, Sweden


Valentin Tudor

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

BES - Differentially Private and Distributed Event Aggregation in Advanced Metering Infrastructures

2nd ACM Cyber-Physical System Security Workshop (CPSS 2016),; (2016)p. 59-69

Paper in proceeding

Employing Private Data in AMI Applications: Short Term Load Forecasting Using Differentially Private Aggregated Data

The 13th IEEE International Conference on Advanced and Trusted Computing, Toulouse, France, 18-21 July 2016,; (2017)p. 404-413

Paper in proceeding

Harnessing the unknown in advanced metering infrastructure traffic

SAC '15 Proceedings of the 30th Annual ACM Symposium on Applied Computing,; (2015)p. 2204-2211

Paper in proceeding

Analysis of the Impact of Data Granularity on Privacy for the Smart Grid

1st ACM Workshop on Language Support for Privacy-Enhancing Technologies, PETShop 2013 - Co-located with the 20th ACM Conference on Computer and Communications Security, CCS 2013; Berlin; Germany; 4 November 2013 through 4 November 2013,; (2013)p. 61-70

Paper in proceeding

A study on data de-pseudonymization in the smart grid

8th European Workshop on System Security, EuroSec 2015; Bordeaux, France,; (2015)

Paper in proceeding

Areas of Advance


Subject Categories

Communication Systems

Media Engineering

Electrical Engineering, Electronic Engineering, Information Engineering

Computer Systems



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4313



Room EC, EDIT Building

Opponent: Simone Fischer-Hübner, University of Karlstad, Sweden

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