A Randomized Response Model for Privacy Preserving Smart Metering

Wang, Shuang; Cui, Lijuan; Que, Jialan; Choi, Dae-Hyun; Jiang, Xiaoqian; Cheng, Samuel; Xie, Le

doi:10.1109/tsg.2012.2192487

Cited by 70 publications

(38 citation statements)

References 26 publications

(31 reference statements)

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“…increase) its consumption, and thus, in practice it is not possible to achieve a constant rate. Anonymization Anonymization techniques allow the service provider to obtain meter data from users without being able to tell apart the meter readings that belong to each individual user [16,51]. This technique can be useful for applications such as forecasting, leak detection or flow monitoring, where knowing the consumption of each user may not be needed.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving smart metering revisited

Rial

Danezis

Kohlweiss

2016

Int. J. Inf. Secur.

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Privacy-preserving billing protocols are useful in settings where a meter measures user consumption of some service, such as smart metering of utility consumption, payas-you-drive insurance and electronic toll collection. In such settings, service providers apply fine-grained tariff policies that require meters to provide a detailed account of user consumption. The protocols allow the user to pay to the service provider without revealing the user's consumption measurements. Our contribution is twofold. First, we propose a general model where a meter can output meter readings to multiple users, and where a user receives meter readings from multiple meters. Unlike previous schemes, our model accommodates a wider variety of smart metering applications. Second, we describe a protocol based on polynomial commitments that improves the efficiency of previous protocols for tariff policies that employ splines to compute the price due.

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Section: Related Workmentioning

confidence: 99%

Privacy-preserving smart metering revisited

Rial

Danezis

Kohlweiss

2016

Int. J. Inf. Secur.

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“…Night Dragon was an intrusion ostensibly originating in China [6] and aimed at probing industrial control systems of energy companies (oil, gas, and petrochemical) in the United States. The attacks used a combination of social engineering and vulnerabilities in remote administration tools on Windows platforms to break into critical computers on the network to gather proprietary information including documents related to oil and gas field exploration and business negotiations as well as details of SCADA systems.…”

Section: Reported Attacks On Electric Gridsmentioning

confidence: 99%

“…Note that utilizing a rechargeable battery does not mean that the load peak or energy consumption profile could be completely hidden, but the inference from the metering data could be significantly limited. Similarly, Wang et al [6] proposed a protocol to enable individual meters to report the true energy consumption readings with a predetermined probability. The randomized response model also obfuscated the metering data so as to prevent the inference of individual households' electricity consumption patterns.…”

Section: Metering Data Protectionmentioning

confidence: 99%

Security Challenges in Smart Grid Implementation

Goel

Yuan

2015

SpringerBriefs in Cybersecurity

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The smart grid architecture amalgamates the physical power grid and a communication grid into a single monolithic network. It poses several security threats that are well known (Li et al. in IEEE Trans Smart Grid 3:1540-1551 [1], McDaniel and McLaughlin in IEEE Secur Priv 7:75, 77, 2009 [2], Bisoi and Dash 2011 [3]). However, it faces unknown threats from the cyber-physical interfaces whereby either cyber-threats can lead to actuation of physical devices or vice versa if physical devices could be manipulated to disrupt the communication infrastructure. The most prevalent threats to the operation and safety of the smart grid come from physical destruction of infrastructure, data poisoning, denial of services, malware, and intrusion. The most prevalent threat to the consumer is breach of privacy of the data and malicious control of personal devices and appliances. This chapter articulates the smart grid architecture and the cyberphysical threats to which the smart grid is vulnerable. Smart Grid Architecture IntroductionThe smart grid is a traditional power grid with a communication network overlaid on top of the traditional power grid. The communication and power grid are interrelated such that the communication network depends on the power grid for data and the power grid depends on the communication for operational activities. The role of the grid is to provide ubiquitous communication capability for collecting data from sensors and meters, process it in situ, and provide pertinent information to support multiple activities such as ensuring grid stability, detecting and resolving anomalies, forecasting load, and facilitating demand response. All this needs to be done while protecting the privacy of the consumers, protecting critical operational data that from national adversaries, and ensuring the integrity of the data for both business and operational needs. This is not a trivial challenge for several reasons, including need to integrate disparate communication media into a single monolithic network, need to provide guaranteed latency and bandwidth for several applications, and need to ensure privacy and security of the data as necessary.The power grid is typically segregated into transmission, distribution, and the last mile. Transmission carries high-voltage current over long distances to substations. Distribution carries lower-voltage data from substations to local transformers. The last mile connects the local transformers to consumers, and it is where utilities and consumers interact to support real-time management of energy generation, distribution, usage, and efficiency. With the integration of the smart grid technologies, the traditional network is now entering households and businesses. Parallel to the power grid, the communication grid can be segregated into wide area network (WAN), metropolitan area network (MAN), field area network (FAN), and home area network (HAN) as shown in Fig. 1.1.The primary goal associated with the transmission network is to provide situational awareness where techn...

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“…Third party escrow mechanisms [1,7,20] require the presence of a trusted third party entity whose role is to anonymize the data collected from the customers and then present it to the DSO or to aggregate the data and present it in an anonymized form. As mentioned in Section 2.1, Efthymiou and Kalogridis [7] present a solution based on separation of data into attributable low-frequency data, collected seldom and mainly used for billing, and anonymized high-frequency data, collected very often and used for grid operation.…”

Section: Overview Of Smart Grid Privacy Mechanisms In the Literaturementioning

confidence: 99%

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

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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.

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A Randomized Response Model for Privacy Preserving Smart Metering

Cited by 70 publications

References 26 publications

Privacy-preserving smart metering revisited

Privacy-preserving smart metering revisited

Security Challenges in Smart Grid Implementation

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

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