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.
To obtain privacy of residential consumers, a scheme named APED is proposed in @cite_19 . The paper employs a pairwise private stream aggregation. The scheme achieves privacy preserving aggregation and also executes error detection when some nodes fail to function normally. DG-APED is an improved form of APED, suggested in @cite_7 . DG-APED propounds diverse grouping-based protocol with error detection. This research added differential privacy technique to APED. Moreover, DG-APED has an advantage of being efficient in terms of communication and computation overhead compared to APED.
@cite_19 @cite_7
2091760837 1902335584
Smart grid, as the next generation of power grid characterized with “two-way” communications, has been paid great attention to realize green, reliable and efficient electricity delivery for our future lives. In order to support the “two-way” communications in smart grid, a large number of smart meters should be deployed at customers to report their near real-time data to control center for monitoring. However, this kind of real-time report could disclose users' privacy, bringing down the users' willingness to participate in smart grid. In order to address the challenge, in this paper, we propose an efficient aggregation protocol with error detection, named APED, for secure smart grid communications. The proposed APED protocol employs a pairwise private stream aggregation scheme to not only achieve privacy-preserving aggregation but also perform error detection when some smart meters are malfunctioning. Detailed security analysis has shown that the proposed APED protocol can guarantee the security and privacy of smart grid communications. In addition, performance evaluation demonstrates its efficiency in terms of computation and communication cost. Smart grid, as the next generation of power grid characterized by “two-way” communications, has been paid great attention to realizing green, reliable, and efficient electricity delivery for our future lives. In order to support the two-way communications in smart grid, a large number of smart meters (SMs) should be deployed to customers to report their near real-time data to control center for monitoring purpose. However, this kind of real-time report could disclose users’ privacy, bringing down the users’ willingness to participate in smart grid. In order to address the challenge, in this paper, by considering the lifetime of SMs as exponential distribution, we propose a diverse grouping-based aggregation protocol with error detection (DG-APED), which employs differential privacy technique into grouping-based private stream aggregation for secure smart grid communications. DG-APED can not only achieve privacy-preserving aggregation, but also perform error detection efficiently when some SMs are malfunctioning. Detailed security analysis shows that DG-APED can guarantee the security and privacy requirements of smart grid communications. In addition, extensive performance evaluation also verifies the effectiveness and efficiency of the proposed DG-APED.