Training arima time-series models under fully homomorphic encryption using approximating polynomials

An example system can include a processor to receive a ciphertext including a fully homomorphic encrypted (FHE) time series from a client device. The processor can train an ARIMA model on the ciphertext using an estimated error and approximating polynomials. The processor can generate an encrypted report and send the encrypted report to the client device.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-08-29
DOI
https://doi.org/10.5281/zenodo.14252816
Primary Topic
Chaos-based Image/Signal Encryption
Type
article
Field-Weighted Citation Impact
0.00
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article

Training arima time-series models under fully homomorphic encryption using approximating polynomials

Zenodo (CERN European Organization for Nuclear Research)
Chaos-based Image/Signal Encryption
article

Training arima time-series models under fully homomorphic encryption using approximating polynomials

article en

Abstract

An example system can include a processor to receive a ciphertext including a fully homomorphic encrypted (FHE) time series from a client device. The processor can train an ARIMA model on the ciphertext using an estimated error and approximating polynomials. The processor can generate an encrypted report and send the encrypted report to the client device.

Zenodo (CERN European Organization for Nuclear Research)
IBM Research - Haifa (IL)
Openalex Percentile: Top 67%
Chaos-based Image/Signal Encryption
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Training arima time-series models under fully homomorphic encryption using approximating polynomials · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS