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.