A privacy-preserving sharing method of electricity usage using self-organizing map

ICT Express - Tập 4 - Trang 24-29 - 2018
Yuichi Nakamura1, Keiya Harada2, Hiroaki Nishi2
1School of Science and Technology, Graduate School of Keio University, Japan
2Department of System Design Engineering, Keio University, Japan

Tài liệu tham khảo

Marisa, 2012, Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems, Neurocomputing, 96, 66, 10.1016/j.neucom.2011.10.037 Christian Neureiter, Gunther Eibl, Armin Veichtlbauer, Dominik Engel, Towards a framework for engineering smart-grid-specific privacy requirements, in: 39th Annual Conference of the IEEE Industrial Electronics Society, 2013, pp. 4803–4808. Craig Gentry, Fully homomorphic encryption using ideal lattices, in: STOC, 2009, pp. 169–178. Zhou, 2015, PPDM: A privacy-preserving protocol for cloud-assisted e-healthcare systems, IEEE J. Sel. Top. Sign. Proces., 9, 1332, 10.1109/JSTSP.2015.2427113 LinkData, “Link and publish your data | Open data sharing,” http://linkdata.org/ (accessed November 2017). The U.S. General Services Administration, “Data.gov,” https://www.data.gov/ (accessed November 2017). Kohonen, 1982, Self-organized formation of topologically correct feature maps, Biol. Cybernet., 43, 59, 10.1007/BF00337288 Majid Bashir Malik, M. Asger Ghazi, Rashid Ali, Privacy preserving data mining techniques: current scenario and future prospects, in: Third International Conference on Computer and Communication Technology, ICCCT, 2012, pp. 26–32. Zhitao Guan, Guanlin Si, Xiaojiang Du, Peng Liu, Zijian Zhang, Zhenyu Zhou, Protecting user privacy based on secret sharing with fault tolerance for big data in smart grid, in: IEEE International Conference on Communications, ICC, 2017, pp. 1–6. S. Chidambaram, K.G. Srinivasagan, A combined random noise perturbation approach for multi-level privacy preservation in data mining, in: 2014 International Conference on Recent Trends in Information Technology, 2014, pp. 1–6. Klaus Kursawe, George Danezis, Markulf Kohlweiss, Privacy-friendly aggregation for the smart-grid, in: International Symposium on Privacy Enhancing Technologies Symposium, PETS, 2011, pp. 175–191. Ji-Won Byun, Ashish Kamra, Elisa Bertino, Ninghui Li, Efficient k-anonymization using clustering techniques, in: Proceedings of the 12th International Conference on Database Systems for Advanced Applications, 2007, pp. 188–200. Kengo Okada, Kanae Matsui, Jan Haase, Hiroaki Nishi, Privacy-preserving data collection for demand response using self-organizing map, in: IEEE 13th International Conference on Industrial Informatics, INDIN, 2015, pp. 652–657. Kanungo, 2002, An efficient k-means clustering algorithm: analysis and implementation, IEEE Trans. Pattern Anal. Mach. Intell., 24, 881, 10.1109/TPAMI.2002.1017616 Paillier, 1999, Public-key cryptosystems based on composite degree residuosity classes, 223 Elgamal, 1985, A public key cryptosystem and a signature scheme based on discrete logarithms, IEEE Trans. Inform. Theory, 31, 469, 10.1109/TIT.1985.1057074 Box, 1976 Irish Social Science Data Archive, “Data from the commission for energy regulation,” http://www.ucd.ie/issda/data/commissionforenergyregulationcer/ (accessed November 2017). Adeline Johnsana, 2016, CATs-clustered k-anonymization of time series data with minimal information loss and optimal re-identification risk, Indian J. Sci. Technol., 9, 1 Rajalakshmi, 2014, Anonymization by data relocation using sub-clustering for privacy preserving data mining, Indian J. Sci. Technol., 7, 975, 10.17485/ijst/2014/v7i7.17