Abstract
In general, as the technology developed, big data analysis provides opportunities to reduce the implementation time and budget. Compared with traditional methods, big data analysis has a greater advantage. It seeks to establish a privacy framework consistent with the factors and measures of mutual understanding in the context of random projection technology. Privacy concerns and perceived benefits have proven to greatly influence personal data protection. The success of stochastic projection techniques depends on voter privacy and personal data protection needs being met. It explores independent component analysis as a possible tool for breaking privacy with deterministic multiplicative perturbation-based models, for example: random orthogonal transformations and random rotations. An approach based on approximate random projection is then proposed that improves privacy protection while maintaining certain date statistical characteristics.
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References
Conti, A., Delbon, P., Laffranchi, L., Paganelli, C., De. Ferrari, F.: Hiv-positive status and preservation of privacy: a recent decision from the italian data protection authority on the procedure of gathering personal patient data in the dental office. J. Med. Ethics 38(6), 386–388 (2012)
Ming, H., Guohua, C., Wenhui, L., et al.: Research on cloud data storage security and privacy protection strategies in the Internet of Things environment. Comput. Sci. 039(005), 62–65 (2012)
Jiang, L., Guo, D.: Dynamic encrypted data sharing scheme based on conditional proxy broadcast re-encryption for cloud storage. IEEE Access 1 (2017)
Yahui, L., Tieying, Z., Xiaolong, J., et al.: Personal privacy protection in the era of big data. Comput. Res. Dev. 52(1), 229–247 (2015)
Tchernykh, A., Babenko, M., Chervyakov, N., et al.: AC-RRNS: anti-collusion secured data sharing scheme for cloud storage. Int. J. Approx. Reason. 102, 60–73 (2018)
Halik, U., Smaczyński, M.: Geovisualisation of relief in a virtual reality system on the basis of low-level aerial imagery. Pure Appl. Geophys. 175(9), 3209–3221 (2018)
Lan Lihui, J., Shiguang, J.H., et al.: A review of privacy protection research in data publishing. Comput. Appl. Res. 08, 28–33 (2010)
Xinling, T., Zhixiao, H.: The paradox of “public data opening” and “personal privacy protection.” News Univ. 6, 55–61 (2014)
Liu, C., Ranjan, R., Yang, C., et al.: MuR-DPA: top-down levelled multi-replica merkle hash tree based secure public auditing for dynamic big data storage on cloud. IEEE Trans. Comput. 64(9), 2609–2622 (2015)
Xuehui, W., Xin, Z.: Exploration on the integration and protection of public-private law of privacy rights—from the perspective of “big data era” personal information privacy. Hebei Law 5, 63–71 (2015)
None: On the orthogonal transformation used for structural comparisons. Acta Crystallograph. Sec. A Found. Crystallography 45(2), 208–210 (2010)
Comon, P.: Independent component analysis, a new concept?. Signal Process. 36(3), 287–314 (1994)
Johnson, W.: Extensions of lipshitz mapping into hilbert space. Contemporary Math. p. 26 (1984)
Acknowledgement
The work in this paper is supported by the science and technology project of State Grid Corporation of China: “Research on credit and digital finance supporting technology based on power big data” (Grand No.5700-202072180A-0–0-00).
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Shen, W., Guo, Q., Zhu, H., Tang, K., Zhan, S., Hao, Z. (2021). The Privacy Data Protection Model Based on Random Projection Technology. In: Tian, Y., Ma, T., Khan, M.K. (eds) Big Data and Security. ICBDS 2020. Communications in Computer and Information Science, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-3150-4_19
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DOI: https://doi.org/10.1007/978-981-16-3150-4_19
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