Digital marketing program design based on abnormal consumer behavior data classification and improved homomorphic encryption algorithm.

ABSTRACT: This article endeavors to delve into the conceptualization of a digital marketing framework grounded in consumer data and homomorphic encryption. The methodology entails employing GridSearch to harmonize and store the leaf nodes acquired post-training of the CatBoost model. These leaf node data subsequently serve as inputs for the radial basis function (RBF) layer, facilitating the mapping of leaf nodes into the hidden layer space. This sequential process culminates in the classification of user online consumption data within the output layer. Furthermore, an enhancement is introduced to the conventional homomorphic encryption algorithm, bolstering privacy preservation throughout the processing of consumption data. This augmentation broadens the applicability of homomorphic encryption to e

SUBMITTER: Cui J 

PROVIDER: S-EPMC10703031 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature