Smart metering de-pseudonymization

Jawurek, Marek; Johns, Martin; Rieck, Konrad

doi:10.1145/2076732.2076764

Cited by 73 publications

(41 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, for the consumer, it represents a privacy invasion [10,11]. Previous studies [8,9,13,15,16,17,19,22] have shown that and how information about a household and its inhabitants can be inferred from its high-resolution energy consumption data. Furthermore, any viable solution for forecasting consumption must also anticipate failing smart meters or communication links.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Privacy-Preserving Statistics

Jawurek¹,

Kerschbaum²

2012

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Privacy-Preserving Statistics

Jawurek¹,

Kerschbaum²

2012

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Jawurek et al [9] present the problem of breaking smart meter privacy by using de-pseudonymization. They propose a framework based on machine learning with support vector machines for the analysis of consumption traces and tracking consumption traces across different pseudonyms by using two linking procedures.…”

Section: Overview Of Smart Grid Privacy Mechanisms In the Literaturementioning

confidence: 99%

“…Out of the presented papers above, the ones that are most closely related to ours are [3,4,7,9]. Efthymiou and Kalogridis [7] set up the terminology on which we build our framework.…”

Section: Related Work Especially Relevant To This Papermentioning

confidence: 99%

“…Their solution is based on a trusted third party that takes care of the private IDs used in the process of high-frequency data anonymization and also of the connections between the high-frequency ID and the low-frequency one. Jawurek et al [9] present a de-pseudonymization framework based on machine learning and are focusing mainly on anomalies in data consumption that happen in the same time. For their solution, fine-grained data is required, because such anomalies can be missed if aggregated daily or monthly values are used.…”

Section: Related Work Especially Relevant To This Papermentioning

confidence: 99%

See 1 more Smart Citation

Analysis of the impact of data granularity on privacy for the smart grid

Tudor

Almgren

Papatriantafilou

2013

Proceedings of the 12th ACM Workshop on Workshop on Privacy in the Electronic Society

View full text Add to dashboard Cite

The upgrade of the electricity network to the "smart grid" has been intensified in the last years. The new automated devices being deployed gather large quantities of data that offer promises of a more resilient grid but also raise privacy concerns among customers and energy distributors.In this paper, we focus on the energy consumption traces that smart meters generate and especially on the risk of being able to identify individual customers given a large dataset of these traces. This is a question raised in the related literature and an important privacy research topic. We present an overview of the current research regarding privacy in the Advanced Metering Infrastructure. We make a formalization of the problem of de-anonymization by matching low-frequency and high-frequency smart metering datasets and we also build a threat model related to this problem. Finally, we investigate the characteristics of these datasets in order to make them more resilient to the de-anonymization process.Our methodology can be used by electricity companies to better understand the properties of their smart metering datasets and the conditions under which such datasets can be released to third parties.

show abstract

“…This is done by homomorphic encryption, i.e., encrypting the readings in a way that the encrypted ciphertexts can be added up, and the sum of ciphertexts -and only the sum-can then be deciphered to show the sum of the plaintexts. This approach, where applicable, is superior to collecting data and then restricting access to it (which creates a security problem as well, and has failed numerous times in the past) or anonymizing the smart grid data, which has also been shown to be insufficient [2]. To go one step further and make this technology applicable for a real roll-out, it is vital to show these advantages, demonstrate how aggregation protocols can be used in a real setting, and validate applicability, performance, and robustness.…”

Section: Introductionmentioning

confidence: 99%

Implementation of privacy-friendly aggregation for the smart grid

Defend¹,

Kursawe²

2013

Proceedings of the First ACM Workshop on Smart Energy Grid Security

View full text Add to dashboard Cite

Abstract. In recent years a number of protocols have been suggested towards privacy-preserving aggregation of smart meter data, allowing electricity network operators to perform a large part of grid maintenance and administrative operations without having to touch any privacy-sensitive data. In light of upcoming European legislation, this approach has gained quite some attention. However, to allow such protocols to have a chance to make it into a real system, it is vital to add credibility by demonstrating that the approach scales, is reasonably robust, and can be integrated into the existing and planned smart metering chains. This paper presents results from integration and scalability tests performed on 100 DLMS/COSEM smart meters in collaboration with a meter manufacturer and a Dutch utility. We outline the lessons learned and choices that had to be made to allow the protocols to run in a real system, as well as some privacy challenges that cannot be covered by this technology.

show abstract

Smart metering de-pseudonymization

Cited by 73 publications

References 16 publications

Fault-Tolerant Privacy-Preserving Statistics

Fault-Tolerant Privacy-Preserving Statistics

Analysis of the impact of data granularity on privacy for the smart grid

Implementation of privacy-friendly aggregation for the smart grid

Contact Info

Product

Resources

About