@inproceedings{saini-toshniwal-2024-privacy,
title = "Privacy Preservation in Federated Market Basket Analysis using Homomorphic Encryption",
author = "Saini, Sameeka and
Toshniwal, Durga",
editor = "Mitkov, Ruslan and
Ezzini, Saad and
Ranasinghe, Tharindu and
Ezeani, Ignatius and
Khallaf, Nouran and
Acarturk, Cengiz and
Bradbury, Matthew and
El-Haj, Mo and
Rayson, Paul",
booktitle = "Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
month = jul,
year = "2024",
address = "Lancaster, UK",
publisher = "International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
url = "https://aclanthology.org/2024.nlpaics-1.13/",
pages = "109--118",
abstract = "Our proposed work introduces a novel approach to privacy-preserving federated learning market basket analysis using Homomorphic encryption. By encrypting frequent mining operations using Homomorphic encryption, our method ensures data privacy without compromising analysis efficiency. Experiments on diverse datasets validate its effectiveness in maintaining data integrity while preserving privacy."
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<abstract>Our proposed work introduces a novel approach to privacy-preserving federated learning market basket analysis using Homomorphic encryption. By encrypting frequent mining operations using Homomorphic encryption, our method ensures data privacy without compromising analysis efficiency. Experiments on diverse datasets validate its effectiveness in maintaining data integrity while preserving privacy.</abstract>
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%0 Conference Proceedings
%T Privacy Preservation in Federated Market Basket Analysis using Homomorphic Encryption
%A Saini, Sameeka
%A Toshniwal, Durga
%Y Mitkov, Ruslan
%Y Ezzini, Saad
%Y Ranasinghe, Tharindu
%Y Ezeani, Ignatius
%Y Khallaf, Nouran
%Y Acarturk, Cengiz
%Y Bradbury, Matthew
%Y El-Haj, Mo
%Y Rayson, Paul
%S Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%D 2024
%8 July
%I International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%C Lancaster, UK
%F saini-toshniwal-2024-privacy
%X Our proposed work introduces a novel approach to privacy-preserving federated learning market basket analysis using Homomorphic encryption. By encrypting frequent mining operations using Homomorphic encryption, our method ensures data privacy without compromising analysis efficiency. Experiments on diverse datasets validate its effectiveness in maintaining data integrity while preserving privacy.
%U https://aclanthology.org/2024.nlpaics-1.13/
%P 109-118
Markdown (Informal)
[Privacy Preservation in Federated Market Basket Analysis using Homomorphic Encryption](https://aclanthology.org/2024.nlpaics-1.13/) (Saini & Toshniwal, NLPAICS 2024)
ACL