Cryptology ePrint Archive: Report 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.

Category / Keywords: implementation / Homomorphic Encryption, CKKS, Goldschmidt’s Algorithm, PCA

Original Publication (with minor differences): CSCML- 2021
DOI:
https://doi.org/10.1007/978-3-030-78086-9 _ 4

Date: received 6 Jul 2021

Contact author: samanvaya panda at research iiit ac in

Available format(s): PDF | BibTeX Citation

Version: 20210708:135751 (All versions of this report)

Short URL: ia.cr/2021/914


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