Privacy-preserving parallel kNN classification algorithm using index-based filtering in cloud computing.
PLoS One
; 17(5): e0267908, 2022.
Article
em En
| MEDLINE
| ID: mdl-35511912
With the development of cloud computing, interest in database outsourcing has recently increased. In cloud computing, it is necessary to protect the sensitive information of data owners and authorized users. For this, data mining techniques over encrypted data have been studied to protect the original database, user queries and data access patterns. The typical data mining technique is kNN classification which is widely used for data analysis and artificial intelligence. However, existing works do not provide a sufficient level of efficiency for a large amount of encrypted data. To solve this problem, in this paper, we propose a privacy-preserving parallel kNN classification algorithm. To reduce the computation cost for encryption, we propose an improved secure protocol by using an encrypted random value pool. To reduce the query processing time, we not only design a parallel algorithm, but also adopt a garbled circuit. In addition, the security analysis of the proposed algorithm is performed to prove its data protection, query protection, and access pattern protection. Through our performance evaluation, the proposed algorithm shows about 2â¼25 times better performance compared with existing algorithms.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Privacidade
/
Computação em Nuvem
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article