Abstract
Database outsourcing has been popular according to the development of cloud computing. Databases need to be encrypted before being outsourced to the cloud so that they can be protected from adversaries. However, the existing kNN classification scheme over encrypted databases in the cloud suffers from high computation overhead. So we proposed a secure and efficient kNN classification algorithm using encrypted index search and Yao’s garbled circuit over encrypted databases. Our algorithm not only preserves data privacy, query privacy, and data access pattern. We show that our algorithm achieves about 17x better performance on classification time than the existing scheme, while preserving high security level.
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Acknowledgment
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number 2016R1D1A3B03935298). This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0113-17-0005, Development of an Unified Data Engineer-ing Technology for Large-scale Transaction Processing and Real-time Complex Analytics).
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Kim, HJ., Shin, JH., Chang, JW. (2018). A Secure and Efficient kNN Classification Algorithm Using Encrypted Index Search and Yao’s Garbled Circuit over Encrypted Databases. In: Dang, T., Küng, J., Wagner, R., Thoai, N., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2018. Lecture Notes in Computer Science(), vol 11251. Springer, Cham. https://doi.org/10.1007/978-3-030-03192-3_3
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DOI: https://doi.org/10.1007/978-3-030-03192-3_3
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