Paper 2021/914

Principal Component Analysis using CKKS Homomorphic Encryption Scheme

Samanvaya Panda

Abstract

Principal component analysis(PCA) is one of the most pop-ular linear dimensionality reduction techniques in machine learning. Inthis paper, we try to present a method for performing PCA on encrypted data using a homomorphic encryption scheme. In a client-server model where the server performs computations on the encrypted data,it (server) does not require to perform any matrix operations like multiplication, inversion, etc. on the encrypted data. This reduces the number of computations significantly since matrix operations on encrypted data are very computationally expensive. For our purpose, we used the CKKS homomorphic encryption scheme since it is most suitable for machine learning tasks allowing approximate computations on real numbers.We also present the experimental results of our proposed Homomorphic PCA(HPCA) algorithm on a few datasets. We measure the R2 score on the reconstructed data and use it as an evaluation metric for our HPCA algorithm.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Published elsewhere. MINOR revision.CSCML- 2021
DOI
10.1007/978-3-030-78086-9_4
Keywords
Homomorphic EncryptionCKKSGoldschmidt’s AlgorithmPCA
Contact author(s)
samanvaya panda @ research iiit ac in
History
2022-04-02: revised
2021-07-08: received
See all versions
Short URL
https://ia.cr/2021/914
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2021/914,
      author = {Samanvaya Panda},
      title = {Principal Component Analysis using CKKS Homomorphic Encryption Scheme},
      howpublished = {Cryptology ePrint Archive, Paper 2021/914},
      year = {2021},
      doi = {10.1007/978-3-030-78086-9_4},
      note = {\url{https://eprint.iacr.org/2021/914}},
      url = {https://eprint.iacr.org/2021/914}
}
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