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Privacy-preserving Association Rule Mining Algorithm Based on FP-Growth in Cloud Computing Environment
JaeHwan Shin, Hyeong-Jin Kim, JaeWoo Chang, Young-Ho Song
http://doi.org/10.5626/JOK.2020.47.8.707
Recently, with the advancement of cloud computing technology, database owners can outsource their databases to the cloud for professional management of data at low cost. However, outsourcing the original database to the cloud server exposes sensitive information in the database, such as banking and medical treatment. In this paper, we propose a privacy-preserving association rule mining algorithm based on the FP-Growth in the cloud computing environment. To protect the sensitive information, the proposed algorithm encrypts the original data and the user"s queries with homomorphic encryption schemes that support specific operations on cipher-texts. To provide efficient query processing on cipher-texts, we propose a comparison operation protocol that compares ciphertexts without exposing the original data. Through the performance evaluation, the proposed algorithm shows approximately 68～123% performance improvement, compared to the existing algorithm.
A Spatial Transformation Scheme Supporting Data Privacy and Query Integrity for Outsourced Databases
Hyeong-Il Kim, Young-Ho Song, Jaewoo Chang
Due to the popularity of location-based services, the amount of generated spatial data in daily life has been dramatically increasing. Therefore, spatial database outsourcing has become popular for data owners to reduce the spatial database management cost. The most important consideration in database outsourcing is meeting the privacy requirements and guarantying the integrity of the query result. However, most of existing database transformation techniques do not support both of the data privacy and integrity of the query result. To solve this problem, we propose a spatial data transformation scheme that utilizes the shearing transformation with rotation shifting. In addition, we described the attack models to measure the data privacy of database transformation schemes. Finally, we demonstrated through the experimental evaluations that our scheme provides high level of data protection against different kinds of attack models, compared to the existing schemes, while guaranteeing the integrity of the query result sets.
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