MICCAI 2022, 25th International Conference on Medical Image Computing and Computer Assisted Intervention, 22 September 2022, Singapore, Singapore
The goal of Privacy Preserving Machine Learning (PPML) is to identify customized algorithms that would, by design, preserve the privacy of the processed data. Fully homomorphic encryption or secure multi-party computation are popular cryptographic techniques for PPML. Yet, these often incur high computational and/or communication costs. In this talk, we will analyse the tension between ML techniques and relevant cryptographic tools, and overview existing solutions addressing privacy requirements.
Type:
Tutorial
City:
Singapore
Date:
2022-09-22
Department:
Digital Security
Eurecom Ref:
7043
Copyright:
© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in MICCAI 2022, 25th International Conference on Medical Image Computing and Computer Assisted Intervention, 22 September 2022, Singapore, Singapore and is available at :
See also:
