Paper 2024/849
Fast, Large Scale Dimensionality Reduction Schemes Based on CKKS
Abstract
The proliferation of artificial intelligence and big data has resulted in a surge in data demand and increased data dimensionality. This escalation has consequently heightened the costs associated with storage and processing. Concurrently, the confidential nature of data collected by various institutions, which cannot be disclosed due to personal privacy concerns, has exacerbated the challenges associated with data analysis and machine learning model training. Therefore, designing a secure and efficient high-dimensional data reduction method that supports multi-party joint participation becomes critical to solving these problems.
This paper proposes a novel homomorphic encryption dimensionality reduction scheme (HE-DR) based on CKKS, which modifies the Rank-Revealing (RR) method to make it more applicable to fully homomorphic encryption, thereby achieving fast and secure dimension reduction for high-dimensional data. Compared to traditional homomorphic encryption dimensionality reduction schemes, our approach does not transmit the user’s original data to other participants in any format (Ciphertext or Plaintext). Moreover, our method's computational efficiency is nearly
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint.
- Keywords
- high-dimensionalefficientdimension reductionhomomorphic encryptionCKKS scheme
- Contact author(s)
-
yuanhaonan @ cigit ac cn
wuwenyuan @ cigit ac cn
chenjingwei @ cigit ac cn - History
- 2024-07-09: revised
- 2024-05-30: received
- See all versions
- Short URL
- https://ia.cr/2024/849
- License
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CC BY
BibTeX
@misc{cryptoeprint:2024/849, author = {Haonan Yuan and Wenyuan Wu and Jingwei Chen}, title = {Fast, Large Scale Dimensionality Reduction Schemes Based on {CKKS}}, howpublished = {Cryptology {ePrint} Archive, Paper 2024/849}, year = {2024}, url = {https://eprint.iacr.org/2024/849} }