Fook Mun Chan

Title: Fook Mun Chan: Innovator in Privacy-Preserving Logistic Regression

Introduction

Fook Mun Chan is a notable inventor based in Singapore, recognized for his contributions to the field of privacy-preserving data analysis. His innovative approach focuses on enhancing the security and efficiency of logistic regression training through advanced encryption techniques.

Latest Patents

Fook Mun Chan holds a patent for a method and system for privacy-preserving logistic regression training based on homomorphically encrypted ciphertexts. This patent outlines a process that includes obtaining a first packed ciphertext containing a portion of a training data sample, as well as a second packed ciphertext comprising weights of the logistic regression model. The method determines output probabilities and updates weights based on these probabilities, ensuring that sensitive data remains protected throughout the training process.

Career Highlights

Fook Mun Chan is affiliated with the Agency for Science, Technology and Research, where he applies his expertise in data security and machine learning. His work is pivotal in advancing the capabilities of privacy-preserving technologies, making significant strides in the field.

Collaborations

Fook Mun Chan collaborates with esteemed colleagues such as Chao Jin and Khin Mi Mi Aung, contributing to a dynamic research environment focused on innovative solutions in data privacy.

Conclusion

Fook Mun Chan's work exemplifies the intersection of technology and privacy, showcasing how innovative methods can enhance data security in logistic regression training. His contributions are vital in shaping the future of secure data analysis.