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
1976060656 2187738029
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. 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.