Data Scientist Ke Zhang’s Research Explores Homomorphic Encryption for Privacy-Preserving Marketing
Data Scientist Ke Zhang’s Research Explores Homomorphic Encryption for Privacy-Preserving Marketing
Publish Date: 2026-07-01 04:32:00
Source Domain: macaubusiness.com
A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted consumer data. By combining secure clustering with efficient computation, the study enables accurate customer segmentation while protecting sensitive information, supporting AI-driven marketing that balances analytical performance with evolving data privacy requirements.
— As the digital economy makes consumer data a central business asset, businesses face a growing question: how can they analyze customer data without unnecessarily exposing it? In the paper “Research on the Application of Homomorphic Encryption-Based Machine Learning Privacy Protection Technology in Precision Marketing” presented at the 2025 3rd International Conference on Data Science and Network Security (ICDSNS) and published in the IEEE proceedings, Ke Zhang examines homomorphic encryption as a way to support machine-learning analysis on encrypted consumer data.
The work addresses a practical challenge in modern marketing: preserving the usefulness of consumer data while reducing privacy risk. As data breaches become more common and privacy regulations such as the European Union’s General Data Protection Regulation and the California Consumer Privacy Act shape corporate data practices, companies face pressure to balance analytical value with stronger data protection. Homomorphic encryption allows computation on encrypted data without decrypting it, but broader adoption has been limited by computational cost and the difficulty of handling real-valued data such as transaction amounts.
Zhang’s research builds a privacy-centered customer-segmentation framework around the K-means clustering algorithm, run entirely in the encrypted, or ciphertext, domain. A rational-to-integer encoding method allows the system to compute on non-integer metrics while maintaining encryption efficiency, and a ciphertext-domain pipeline supports customer…
