A Privacy Model for Smart Metering

Bohli, Jens-Matthias; Sorge, Christoph; Ugus, Osman

doi:10.1109/iccw.2010.5503916

Cited by 168 publications

(165 citation statements)

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“…This third party verifies the authenticity of the data, removes the identifying information and forwards it to the consumer of this data. This solution provides privacy by anonymizing information at a trusted third party before forwarding them to the data consumer which requires a similar computational and storage effort as in [1].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, in [1] a model for measuring privacy in smart metering is developed and subsequently two different solutions to privacy are presented: A trusted third party approach, where aggregation takes place at the third party and alternatively the approach of masking individual values with added noise directly at the smart meters which is canceled out in the sum over all meters at the supplier. The trusted third party of their first approach is able to calculate arbitrary statistics.…”

Section: Related Workmentioning

confidence: 99%

“…Privacy-preserving smart metering statistics: Furthermore, we provide an overview on different (non-fault-tolerant) approaches that ensure privacy for different applications of smart metering data: leakage detection [5] by homomorphic encryption, general aggregation [14] and comparison by secret-sharing, aggregation by third party [1,21] or aggregation [1] by randomization.…”

Section: Related Workmentioning

confidence: 99%

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Fault-Tolerant Privacy-Preserving Statistics

Jawurek¹,

Kerschbaum²

2012

Lecture Notes in Computer Science

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Abstract. Real-time statistics on smart meter consumption data must preserve consumer privacy and tolerate smart meter failures. Existing protocols for this private distributed aggregation model suffer from various drawbacks that disqualify them for application in the smart energy grid. Either they are not fault-tolerant or if they are, then they require bidirectional communication or their accuracy decreases with an increasing number of failures. In this paper, we provide a protocol that fixes these problems and furthermore, supports a wider range of exchangeable statistical functions and requires no group key management. A key-managing authority ensures the secure evaluation of authorized functions on fresh data items using logical time and a custom zero-knowledge proof providing differential privacy for an unbounded number of statistics calculations. Our privacy-preserving protocol provides all the properties that make it suitable for use in the smart energy grid.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Fault-Tolerant Privacy-Preserving Statistics

Jawurek¹,

Kerschbaum²

2012

Lecture Notes in Computer Science

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“…Therefore, there is a need for investigating security and customer privacy related to user-generated energy data [92]. Several authors have presented ideas of how to anonymize smart meter data between the customer and third parties [93,94], but those are just singular technical approaches.…”

Section: Privacy Concernsmentioning

confidence: 99%

“…As an example, Figure 4 shows different scales and relevant data for the estimation of the building heat demand. As a consequence, when coupling energy planning and GIS, there are at least three reasons why one would have to work on different spatial scales and to aggregate or disaggregate the data: (1) a common scale (e.g., building level or district level) for all the data is required for the analysis or validation of the results [112][113][114]; (2) a faster computing time is desired [115]; (3) consideration of data privacy aspects are required [93,94].…”

Section: Aggregation: Combining the Energy And Gis Viewsmentioning

confidence: 99%

GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues

Resch

Sagl

Törnros

et al. 2014

IJGI

124

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Abstract:In the face of the broad political call for an -energy turnaround‖, we are currently witnessing three essential trends with regard to energy infrastructure planning, energy generation and storage: from planned production towards fluctuating production on the basis of renewable energy sources, from centralized generation towards decentralized generation and from expensive energy carriers towards cost-free energy carriers. These changes necessitate considerable modifications of the energy infrastructure. Even though most of these modifications are inherently motivated by geospatial questions and challenges, the integration of energy system models and Geographic Information Systems (GIS) is still in its infancy. This paper analyzes the shortcomings of previous approaches in using GIS in renewable energy-related projects, extracts distinct challenges from these previous efforts and, finally, defines a set of core future research avenues for GIS-based energy infrastructure planning with a focus on the use of renewable energy. These future OPEN ACCESS ISPRS Int. J. Geo-Inf. 2014, 3 663 research avenues comprise the availability base data and their -geospatial awareness‖, the development of a generic and unified data model, the usage of volunteered geographic information (VGI) and crowdsourced data in analysis processes, the integration of 3D building models and 3D data analysis, the incorporation of network topologies into GIS, the harmonization of the heterogeneous views on aggregation issues in the fields of energy and GIS, fine-grained energy demand estimation from freely-available data sources, decentralized storage facility planning, the investigation of GIS-based public participation mechanisms, the transition from purely structural to operational planning, data privacy aspects and, finally, the development of a new dynamic power market design.Keywords: integration of GIS and energy system models; GIS and renewable energy; GIS-based energy infrastructure planning; future research challenges; fluctuating renewables; structural planning of local energy systems; operation optimization

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