Abstract
In a smart grid environment, smart meters periodically collect and report information such as electricity consumption of users to a control center for timely monitoring, billing and other analytical purposes. There is, however, a need to ensure the privacy of user data, particularly when the data is combined with data from other sources. In this paper, we propose a new fine-grained data analysis (hereafter referred to as FGDA) scheme for privacy preserving smart grid communications. FGDA is designed to compute multifunctional data analysis (such as average, variance, and skewness) based on users’ ciphertexts, as well as supporting fault tolerance feature. We remark that FGDA can still function when some smart meters fail. Compared to existing schemes providing both the properties of multifunction and fault tolerance, FGDA is more efficient in terms of computation overheads. This is because FDGA does not use bilinear map or Pollard’s lambda method during decryption. We also demonstrate that FGDA achieves a higher communication efficiency, as the gateway only needs to send the ciphertext to the control center once even for different statistical functions.
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Acknowledgments
The work was supported in part by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant No. U1509219, the National Natural Science Foundation of China under Grant No. 61632012, the Shanghai Natural Science Foundation under Grant No. 17ZR1408400, and the Shanghai Sailing Program under Grant No. 17YF1404300. This work and the preparation of this publication were funded in part by monies provided by CPS Energy through an agreement with The University of Texas at San Antonio.
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Ge, S., Zeng, P., Lu, R. et al. FGDA: Fine-grained data analysis in privacy-preserving smart grid communications. Peer-to-Peer Netw. Appl. 11, 966–978 (2018). https://doi.org/10.1007/s12083-017-0618-9
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DOI: https://doi.org/10.1007/s12083-017-0618-9