Data Protection and Privacy Protection of Advertising based on Cloud Computing Platform

Abstract

This paper uses the hybrid leapfrog algorithm to mine user information in encrypted advertisements effectively and intelligently. This method handles the nonlinearity of the original data by mapping it into kernel space. The representation of the original ciphertext in the kernel space is obtained by sparsely reconstructing the encrypted original advertising data. Build a corresponding scoring mechanism and select the best advertising data characteristics. The selected data were clustered using the data fuzzy clustering method based on the improved hybrid leapfrog. Set the adjustment coefficient to improve the local optimization performance of hybrid frog leaping. This algorithm uses the tightness and separation in genetic algorithms and constructs a fitness function to determine the clustering critical value. This enables the practical, intelligent mining of homomorphic passwords with privacy protection. Experimental results show that the method proposed in this article can effectively improve the convergence speed and accuracy of clustering. Improve Blowfish by combining multi-threading, sharing encryption and other methods. This enables encryption and decryption of large amounts of model data. The research of this project has very important research value in improving the security performance and effectiveness of cryptographic algorithms.