Khai phá tập phổ biến đảm bảo tính riêng tư cho dữ liệu phân mảnh dọc sử dụng hệ mật ElGamal
DOI:
https://doi.org/10.54654/isj.v1i16.928Keywords:
privacy-preserving, frequent itemset mining, secure data miningTóm tắt
Tóm tắt— Khai phá dữ liệu đảm bảo tính riêng tư ngày càng nhận sự quan tâm của cộng đồng nghiên cứu, đặc biệt là khai phá tập phổ biến có đảm bảo tính riêng tư là một chủ đề được đề cập nhiều trong thời gian gần đây. Bài báo này, tác giả đề xuất một phương pháp khai phá tập phổ biến đảm bảo tính riêng tư cho mô hình dữ liệu phân mảnh dọc trên 3 thành viên. Phương pháp đề xuất có hiệu quả tương đương và an toàn hơn các phương pháp hiện có, mỗi thành viên tham gia giao thức có thể chống lại sự thông đồng của 2 thành viên còn lại và không làm lộ thông tin trong quá trình thực thi giao thức.
Abstract— Privacy-preserving data mining is increasingly receiving the attention of the research community, especially privacy-preserving frequent set mining, which is a topic that has been discussed a lot in recent times. In this paper, we propose a novel technique for privacy-preserving freuquent itemset mining in three party vertically distributed data. We show that the proposed technique is as efficient as the best existing protocols for performing the same task, and more secure than the most secured protocols with against collusion, 3 members joined the protocol the 2 members colluded not to reveal data of the other member.
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