Paper 2023/1354
Privacy Preserving Feature Selection for Sparse Linear Regression
Abstract
Privacy-Preserving Machine Learning (PPML) provides protocols for learning and statistical analysis of data that may be distributed amongst multiple data owners (e.g., hospitals that own proprietary healthcare data), while preserving data privacy. The PPML literature includes protocols for various learning methods, including ridge regression. Ridge regression controls the
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Major revision. Proceedings on Privacy Enhancing Technologies
- Keywords
- Privacy preserving machine learningsparse linear regressionfeature selectionfully homomorphic encryption
- Contact author(s)
-
adi akavia @ gmail com
benga9 @ gmail com
hayim shaul @ gmail com
mor weiss @ biu ac il
zohar yakhini @ gmail com - History
- 2023-09-11: approved
- 2023-09-11: received
- See all versions
- Short URL
- https://ia.cr/2023/1354
- License
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CC BY
BibTeX
@misc{cryptoeprint:2023/1354, author = {Adi Akavia and Ben Galili and Hayim Shaul and Mor Weiss and Zohar Yakhini}, title = {Privacy Preserving Feature Selection for Sparse Linear Regression}, howpublished = {Cryptology {ePrint} Archive, Paper 2023/1354}, year = {2023}, url = {https://eprint.iacr.org/2023/1354} }