Research on the Application of Homomorphic Encryption-Based Machine Learning Privacy Protection Technology in Precision Marketing

Against the backdrop of the rapid development of the digital economy, as the online traffic dividend gradually fades, enterprises are shifting their strategic focus to the offline market, aiming to extract the deep value of consumer data to drive the intelligent upgrade of precision marketing. The combination of big data and machine learning enables more accurate analysis of user consumption behavior, thereby optimizing customer segmentation and personalized recommendation strategies. In the context of increasing risks of data privacy leakage, how to ensure data security while enhancing the scientific nature of marketing decisions has become a core issue in current research. To address this, this paper first conducts a systematic analysis of the domestic and international research status of machine learning privacy computing, explores the applicability of existing data protection technologies, and introduces vector homomorphic encryption technology to enhance the security of the data sharing process. In line with the demands of precision marketing, this paper designs a method for customer segmentation under privacy protection, using the K-means clustering algorithm to classify consumption data and optimizing the homomorphic encryption scheme to enable it to perform calculations on rational numbers, thereby improving the accuracy and applicability of ciphertext computations. To address the limitations of the K-means algorithm in clustering in the ciphertext domain, this paper proposes an optimization strategy by improving the initial center point selection mechanism, enhancing the accuracy of the clustering results and more precisely targeting the user base. Based on the above theoretical research, this paper further constructs a consumption data analysis system based on homomorphic encryption, designs a secure and efficient system architecture, and verifies the system functions to ensure its feasibility in the precision marketing scenario.