Multi-Party Verifiable Privacy-Preserving Federated k-Means Clustering in Outsourced Environment

التفاصيل البيبلوغرافية
العنوان: Multi-Party Verifiable Privacy-Preserving Federated k-Means Clustering in Outsourced Environment
المؤلفون: Ruiqi Hou, Fei Tang, Shikai Liang, Guowei Ling
المصدر: Security and Communication Networks, Vol 2021 (2021)
بيانات النشر: Hindawi Limited, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Q1-390, Science (General), Article Subject, Computer Networks and Communications, T1-995, Technology (General), Information Systems
الوصف: As a commonly used algorithm in data mining, clustering has been widely applied in many fields, such as machine learning, information retrieval, and pattern recognition. In reality, data to be analyzed are often distributed to multiple parties. Moreover, the rapidly increasing data volume puts heavy computing pressure on data owners. Thus, data owners tend to outsource their own data to cloud servers and obtain data analysis results for the federated data. However, the existing privacy-preserving outsourced k -means schemes cannot verify whether participants share consistent data. Considering the scenarios with multiple data owners and sensitive information security in an outsourced environment, we propose a verifiable privacy-preserving federated k -means clustering scheme. In this article, cloud servers and participants perform k -means clustering algorithm over encrypted data without exposing private data and intermediate results in each iteration. In particular, our scheme can verify the shares from participants when updating the cluster centers based on secret sharing, hash function and blockchain, so that our scheme can resist inconsistent share attacks by malicious participants. Finally, the security and experimental analysis are carried out to show that our scheme can protect private data and get high-accuracy clustering results.
وصف الملف: text/xhtml
تدمد: 1939-0122
1939-0114
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a501fa604d032416f4d290343a27fc47
https://doi.org/10.1155/2021/3630312
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....a501fa604d032416f4d290343a27fc47
قاعدة البيانات: OpenAIRE