Secure and privacy-preserving information aggregation for smart grids

Li, Fengjun; Luo, Bo; Liu, Peng

doi:10.1504/ijsn.2011.039631

Cited by 89 publications

(68 citation statements)

References 32 publications

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“…F. Li et al [6], [7] presented distributed in-network aggregation approaches to efficiently aggregate smart metering data along a spanning tree. However, these approaches do not consider authentication and integrity protection.…”

Section: Related Workmentioning

confidence: 99%

Secure and privacy-preserving concentration of metering data in AMI networks

Saxena

Choi

Grijalva

2017

2017 IEEE International Conference on Communications (ICC)

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Abstract-The industry has recognized the risk of cyberattacks targeting to the advanced metering infrastructure (AMI). A potential adversary can modify or inject malicious data, and can perform security attacks over an insecure network. Also, the network operators at intermediate devices can reveal private information, such as the identity of the individual home and metering data units, to the third-party. Existing schemes generate large overheads and also do not ensure the secure delivery of correct and accurate metering data to all AMI entities, including data concentrator at the utility and the billing center. In this paper, we propose a secure and privacy-preserving data aggregation scheme based on additive homomorphic encryption and proxy re-encryption operations in the Paillier cryptosystem. The scheme can aggregate metering data without revealing the actual individual information (identity and energy usage) to intermediate entities or to any third-party, hence, resolves identity and related data theft attacks. Moreover, we propose a scalable algorithm to detect malicious metering data injected by the adversary. The proposed scheme protects the system against man-in-the-middle, replay, and impersonation attacks, and also maintains message integrity and undeniability. Our performance analysis shows that the scheme generates manageable computation, communication, and storage overheads and has efficient execution time suitable for AMI networks.

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Section: Related Workmentioning

confidence: 99%

Secure and privacy-preserving concentration of metering data in AMI networks

Saxena

Choi

Grijalva

2017

2017 IEEE International Conference on Communications (ICC)

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“…Therefore, data aggregation is a critical operation in the smart grids to reduce the communication overhead. Motivated by the in-network aggregation solutions for wireless sensor networks, several aggregation protocols using additively homomorphic encryption schemes [16,23,24], have been presented to protect end-to-end data confidentiality and privacy against malicious or "curious" meters en route. However, due to the vulnerability of the wireless network in NANs, accidental communication errors or external attackers may alter the transmitted metering data, thus, the data integrity was still in risk.…”

Section: Related Workmentioning

confidence: 99%

“…The collector is responsible for collecting data and verifying the data integrity before relaying them to the control center. Similar as the other in-network aggregation approaches for smart grid data collection, a spanning tree (e.g., BFST [23,24] or MST [21]) is constructed to include all the smart meters in the neighborhood into an aggregation tree. As shown in Fig.…”

Section: Network Modelmentioning

confidence: 99%

“…Li et al first proposed an aggregation approach to encrypt meter data with homomorphic encryption [23], which supports in-network aggregation operations over the encrypted data. Later, several secure in-network aggregation protocols based on modified homomorphic encryption have been proposed to securely and efficiently route the meter data to the collector [14,21,24].…”

Section: Introductionmentioning

confidence: 99%

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Securing Smart Grid In-Network Aggregation through False Data Detection

Yang¹,

Li²

2017

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Existing prevention-based secure in-network data aggregation schemes for the smart grids cannot effectively detect accidental errors and falsified data injected by malfunctioning or compromised meters. In this work, we develop a light-weight anomaly detector based on kernel density estimator to locate the smart meter from which the falsified data is injected. To reduce the overhead at the collector, we design a dynamic grouping scheme, which divides meters into multiple interconnected groups and distributes the verification and detection load among the root of the groups. To enable outlier detection at the root of the groups, we also design a novel data re-encryption scheme based on bilinear mapping so that data previously encrypted using the aggregation key is transformed in a form that can be recovered by the outlier detectors using a temporary re-encryption key. Therefore, our proposed detection scheme is compatible with existing innetwork aggregation approaches based on additive homomorphic encryption. We analyze the security and efficiency of our scheme in terms of storage, computation and communication overhead, and evaluate the performance of our outlier detector with experiments using real-world smart meter consumption data. The results show that the performance of the light-weight detector yield high precision and recall.

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“…Recently, several studies have proposed solutions to this problem in smart grids [3][4][5][6][7][8]. One promising technical solution to protect user privacy is anonymizing each packet of high-frequency metering data by aggregating them at multiple levels (e.g., neighborhood, subdivision, district, and city) via privacy-protecting cryptographic techniques such as homomorphic encryption [9,10].…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Smart Metering with Authentication in a Smart Grid

Hur

Koo

Shin³

2015

Applied Sciences

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Abstract:The traditional security objectives of smart grids have been availability, integrity, and confidentiality. However, as the grids incorporate smart metering and load management, user and corporate privacy is increasingly becoming an issue in smart grid networks. Although transmitting current power consumption levels to the supplier or utility from each smart meter at short intervals has an advantage for the electricity supplier's planning and management purposes, it threatens user privacy by disclosing fine-grained consumption data and usage behavior to utility providers. In this study, we propose a distributed incremental data aggregation scheme where all smart meters on an aggregation path are involved in routing the data from the source meter to the collection unit. User privacy is preserved by symmetric homomorphic encryption, which allows smart meters to participate in the aggregation without seeing any intermediate or final result. Aggregated data is further integrated with an aggregate signature to achieve data integrity and smart meter authentication in such a way that dishonest or fake smart meters cannot falsify data en route. Only the collection unit can obtain the aggregated data and verify its integrity while the individual plain data are not exposed to the collection unit. Therefore, user privacy and security are improved for the smart metering in a smart grid network.Appl. Sci. 2015Sci. , 5 1504

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Secure and privacy-preserving information aggregation for smart grids

Cited by 89 publications

References 32 publications

Secure and privacy-preserving concentration of metering data in AMI networks

Secure and privacy-preserving concentration of metering data in AMI networks

Securing Smart Grid In-Network Aggregation through False Data Detection

Privacy-Preserving Smart Metering with Authentication in a Smart Grid

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