Enhancing Security in CNN-Based Travel Recommendation Models Using CKKS Homomorphic Encryption

Authors

  • Tianhao Chen

DOI:

https://doi.org/10.54097/7ay21z97

Keywords:

Homomorphic Encryption, Convolutional Neural Networks, Travel Recommendations, Data Security, Privacy Preservation

Abstract

This study explores the integration of CKKS homomorphic encryption with convolutional neural networks (CNNs) to enhance the security of travel recommendation systems. By adapting CNN architectures to operate efficiently on encrypted data using CKKS, we address the challenge of maintaining the users’ privacy without compromising system performance. Key results indicate significant improvements in data security with minimal impact on recommendation accuracy.

References

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Published

05-12-2024

Issue

Section

Articles

How to Cite

Chen, T. (2024). Enhancing Security in CNN-Based Travel Recommendation Models Using CKKS Homomorphic Encryption. Journal of Computing and Electronic Information Management, 15(2), 27-29. https://doi.org/10.54097/7ay21z97