Abstract
Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy-preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user's input query, and hides the data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our proposed protocol using a real-world dataset under different parameter settings.
Recommended Citation
B. K. Samanthula et al., "K-nearest Neighbor Classification over Semantically Secure Encrypted Relational Data," IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 5, pp. 1261 - 1273, article no. 6930802, Institute of Electrical and Electronics Engineers, May 2015.
The definitive version is available at https://doi.org/10.1109/TKDE.2014.2364027
Department(s)
Computer Science
Keywords and Phrases
Encryption; k-NN Classifier; Outsourced Databases; Security
International Standard Serial Number (ISSN)
1041-4347
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 May 2015
