As smart grids are getting popular and being widely implemented, preserving the privacy of consumers is becoming more substantial. Power generation and pricing in smart grids depends on the continuously gathered information from the consumers. However, having access to the data relevant to the electricity consumption of each individual consumer is in conflict with its privacy. One common approach for preserving privacy is to aggregate data of different consumers and to use their smart-meters for calculating the bills. But in this approach, malicious consumers who send erroneous data to take advantage or disrupt smart grid cannot be identified. In this paper, we propose a new statistical-based scheme for data gathering and billing in which the privacy of consumers is preserved, and at the same time, if any consumer with erroneous data can be detected. Our simulation results verify these matters.
In @cite_27 authors present a secure power usage data aggregation method for smart grid where the supplier understands usage of each neighborhood and makes decision about energy distribution, while it has no idea of the individual electricity consumption of each consumer. This method is designed to barricade internal attacks and provide batch verification. Authors of @cite_26 found out that @cite_27 has the weakness of key leakage and the imposter can obtain the private key of consumer easily. It is proved that by using the protocol in @cite_26 , key leakage problem is solved and a better performance in terms of computational cost is achieved. Neglecting energy cost is the main disadvantage of this method.
@cite_27 @cite_26
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The widespread deployment of smart meters that frequently report energy consumption information, is a known threat to consumers’ privacy. Many promising privacy protection mechanisms based on secure aggregation schemes have been proposed. Even though these schemes are cryptographically secure, the energy provider has access to the plaintext aggregated power consumption. A privacy trade-off exists between the size of the aggregation scheme and the personal data that might be leaked, where smaller aggregation sizes leak more personal data. Recently, a UK industrial body has studied this privacy trade-off and identified that two smart meters forming an aggregate, are sufficient to achieve privacy. In this work, we challenge this study and investigate which aggregation sizes are sufficient to achieve privacy in the smart grid. Therefore, we propose a flexible, yet formal privacy metric using a cryptographic game based definition. Studying publiclyavailable, real world energy consumption datasets with various temporal resolutions, ranging from minutes to hourly intervals, we show that a typical household can be identified with very high probability. For example, we observe a 50 advantage over random guessing in identifying households for an aggregation size of 20 households with a 15-minutes reporting interval. Furthermore, our results indicate that single appliances can be identified with significant probability in aggregation sizes up to 10 households. With fast advancements of communication, systems and information technologies, a smart grid (SG) could bring much convenience to users because it could provide a reliable and efficient energy service. The data aggregation (DA) scheme for the SG plays an important role in evaluating information about current energy usage. To achieve the goal of preserving users' privacy, many DA schemes for the SG have been proposed in last decade. However, how to withstand attacks of internal adversaries is not considered in those schemes. To enhance preservation of privacy, proposed a DA scheme for the SG against internal adversaries. In 's DA scheme, blinding factors are used in evaluating information about current energy usage and the aggregator cannot get the consumption information of any individual user. demonstrated that their scheme was secure against various attacks. However, we find that their scheme suffers from the key leakage problem, i.e., the adversary could extract the user's private key through the public information. To overcome such serious weakness, this paper proposes an efficient and privacy-preserving DA scheme for the SG against internal attacks. Analysis shows that the proposed DA scheme not only overcome the key leakage problem in 's DA scheme, but also has better performance. Privacy-preserving metering aggregation is regarded as an important research topic in securing a smart grid. In this paper, we first identify and formalize a new attack, in which the attacker could exploit the information about the presence or absence of a specific person to infer his meter readings. This attack, coined as human-factor-aware differential aggregation (HDA) attack, cannot be addressed in existing privacy-preserving aggregation protocols proposed for smart grids. We give a formal definition on it and propose two novel protocols, including basic scheme and advanced scheme, to achieve privacy-preserving smart metering data aggregation and to resist the HDA attack. Our protocol ensures that smart meters periodically upload encrypted measurements to a (electricity) supplier aggregator such that the aggregator is able to derive the aggregated statistics of all meter measurements but is unable to learn any information about the human activities. We present the formal security analysis for the proposed protocol to guarantee the strong privacy. Moreover, we evaluate the performance of our protocol in a Java-based implementation under different parameters. The performance and utility analysis shows that our protocol is simple, efficient, and practical. According to related research, energy consumption can be effectively reduced by using energy management information of smart grids. In smart grid architecture, electricity suppliers can monitor, predicate, and control energy generation consumption in real time. Users can know the current price of electrical energy and obtain energy management information from smart meters. It helps users reduce home's energy use. However, electricity consumptions of users may divulge the privacy information of users. Therefore, privacy of users and communication security of the smart grid become crucial security issues. This paper presents a secure power-usage data aggregation scheme for smart grid. Electricity suppliers can learn about the current power usage of each neighborhood to arrange energy supply and distribution without knowing the individual electricity consumption of each user. This is the first scheme against internal attackers, and it provides secure batch verification. Additionally, the security of the proposed scheme is demonstrated by formal proofs.