@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://preview.aclanthology.org/fix-sig-urls/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."
}