Model-based assessment for balancing privacy requirements and operational capabilities in the smart grid

The smart grid changes the way energy is produced and distributed. In addition both, energy and information is exchanged bidirectionally among participating parties. Therefore heterogeneous systems have to cooperate effectively in order to achieve a common high-level use case, such as smart metering for billing or demand response for load curtailment. Furthermore, a substantial amount of personal data is often needed for achieving that goal. Capturing and processing personal data in the smart grid increases customer concerns about privacy and in addition, certain statutory and operational requirements regarding privacy aware data processing and storage have to be met. An increase of privacy constraints, however, often limits the operational capabilities of the system. In this paper, we present an approach that automates the process of finding an optimal balance between privacy requirements and operational requirements in a smart grid use case and application scenario. This is achieved by formally describing use cases in an abstract model and by finding an algorithm that determines the optimum balance by forward mapping privacy and operational impacts. For this optimal balancing algorithm both, a numeric approximation and - if feasible - an analytic assessment are presented and investigated. The system is evaluated by applying the tool to a real-world use case from the University of Southern California (USC) microgrid.

[1]  Mathias Uslar,et al.  Towards a Model-Driven-Architecture Process for Smart Grid Projects , 2014 .

[2]  Viktor K. Prasanna,et al.  Semantic Information Integration for Smart Grid Applications , 2011 .

[3]  Viktor K. Prasanna,et al.  Model-driven privacy assessment in the smart grid , 2015, 2015 International Conference on Information Systems Security and Privacy (ICISSP).

[4]  Nalini Venkatasubramanian,et al.  Middleware for Pervasive Spaces: Balancing Privacy and Utility , 2009, Middleware.

[5]  Christian Neureiter,et al.  Towards a framework for engineering smart-grid-specific privacy requirements , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[6]  Osmar R. Zaïane,et al.  Algorithms for balancing privacy and knowledge discovery in association rule mining , 2003, Seventh International Database Engineering and Applications Symposium, 2003. Proceedings..

[7]  A. Cavoukian,et al.  SmartPrivacy for the Smart Grid: embedding privacy into the design of electricity conservation , 2010 .

[8]  William H. Sanders,et al.  Go with the flow: toward workflow-oriented security assessment , 2013, NSPW '13.

[9]  H. Nyquist,et al.  Certain Topics in Telegraph Transmission Theory , 1928, Transactions of the American Institute of Electrical Engineers.

[10]  Stephen B. Wicker,et al.  Privacy-Aware Design Principles for Information Networks , 2011, Proceedings of the IEEE.

[11]  Ian Richardson,et al.  Smart meter data: Balancing consumer privacy concerns with legitimate applications , 2012 .

[12]  Patrick D. McDaniel,et al.  Security and Privacy Challenges in the Smart Grid , 2009, IEEE Security & Privacy.