@inproceedings{tkphb-ppcml-22,
  author = {Kristof T'Jonck and Chandrakanth R. Kancharla and Bozheng Pang and Hans Hallez and Jeroen Boydens},
  title = {{Privacy Preserving Classification via Machine Learning Model Inference on Homomorphic Encrypted Medical Data}},
  url = {https://ieeexplore.ieee.org/abstract/document/9920289},
  doi = {10.1109/ET55967.2022.9920289},
  abstract = {This paper studies the use of homomorphic encryption to preserve privacy when using machine learning classifiers. The paper compares different parameters and explores drawbacks in terms of accuracy, speed, and packet size when using encrypted data versus unencrypted data by using a client-server use case. These comparisons were performed during multiple tests on different datasets with different sizes and complexity. These tests show it is possible to do machine learning with homomorphic encrypted data without losing accuracy. However, the increased processing time, data size and communication time have to be considered.},
  urldate = {2024-03-14},
  booktitle = {2022 {XXXI} {International} {Scientific} {Conference} {Electronics} ({ET})},
  month = {September},
  year = {2022},
  keywords = {Complexity theory, Data models, Data privacy, Homomorpic Encryption, Machine learning, Machine Learning, Machine learning algorithms, Privacy, Random access memory},
  pages = {1--6},
}