Abstract
Centralised machine learning brings in side effect pertaining to privacy preservation, most of machine learning methods prone to using the frameworks without privacy protection, as current methods for privacy preservation will slow down model training and testing. In order to resolve this problem, we develop a new noise generating method based on information entropy by using differential privacy for betterment the privacy protection which owns the architecture of federated machine learning. Our experiments unveil that this solution effectively preserves privacy in the vein of centralized federated learning. The gained accuracy is promising which has a room to be uplifted.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)
Agarwal, N., Suresh, A.T., Yu, F.X.X., Kumar, S., McMahan, B.: CPSGD: communication-efficient and differentially-private distributed SGD. In: Advances in Neural Information Processing Systems, pp. 7564–7575 (2018)
Aggarwal, C.C., Yu, P.S.: A general survey of privacy-preserving data mining models and algorithms. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining. Advances in Database Systems, vol. 34, pp. 11–52. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-70992-5_2
Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. In: Advances in Neural Information Processing Systems, pp. 289–296 (2009)
De Brabanter, J., De Moor, B., Suykens, J.A., Van Gestel, T., Vandewalle, J.P.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
Hsieh, F.Y., Bloch, D.A., Larsen, M.D.: A simple method of sample size calculation for linear and logistic regression. Stat. Med. 17(14), 1623–1634 (1998)
Krizhevsky, A., Nair, V., Hinton, G.: The CIFAR-10 Dataset, vol. 55 (2014). http://www.cs.toronto.edu/kriz/cifar.html
Lee, S., Rao, R., Narasimha, R.: Characterization of self-similarity properties of discrete-time linear scale-invariant systems. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221), vol. 6, pp. 3969–3972. IEEE (2001)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)
Lou, Y., Yu, L., Wang, S., Yi, P.: Privacy preservation in distributed subgradient optimization algorithms. IEEE Trans. Cybern. 48(7), 2154–2165 (2017)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: \(l\)-diversity: privacy beyond \(k\)-anonymity. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 3-es (2007)
Margineantu, D.D., Dietterich, T.G.: Pruning adaptive boosting. In: International Conference on Machine Learning, vol. 97, pp. 211–218. ICML (1997)
Richards, M.A.: Coherent integration loss due to white Gaussian phase noise. IEEE Sig. Process. Lett. 10(7), 208–210 (2003)
Shabtai, A., Elovici, Y., Rokach, L.: A Survey of Data Leakage Detection and Prevention Solutions. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-2053-8
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)
Sweeney, L.: \(k\)-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 557–570 (2002)
Vapnik, V.: Principles of risk minimization for learning theory. In: Advances in Neural Information Processing Systems, pp. 831–838 (1992)
Whittle, P.: Estimation and information in stationary time series. Arkiv för matematik 2(5), 423–434 (1953)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)
Wombacher, A.: Data workflow-a workflow model for continuous data processing. Data Process. (2010)
Zhu, Y., Liu, L.: Optimal randomization for privacy preserving data mining. In: Proceedings of the Tenth ACM SIGKDD, pp. 761–766 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ma, B., Yan, W.Q., Lai, E., Wu, J. (2021). A New Noise Generating Method Based on Gaussian Sampling for Privacy Preservation. In: Nguyen, M., Yan, W.Q., Ho, H. (eds) Geometry and Vision. ISGV 2021. Communications in Computer and Information Science, vol 1386. Springer, Cham. https://doi.org/10.1007/978-3-030-72073-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-72073-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72072-8
Online ISBN: 978-3-030-72073-5
eBook Packages: Computer ScienceComputer Science (R0)