Homomorphic Encryption-Based Support Computation for Privacy-Preserving Association Analysis

Yunsoo Park, Lynin Sokhonn, Munkyu Lee

http://doi.org/10.5626/JOK.2024.51.3.203

Homomorphic encryption is a cryptographic scheme that enables computation on ciphertexts without decryption. Homomorphic encryption is attracting attention as a cryptographic technology that can solve the issue of user privacy invasion in machine learning and cloud services. A representative scheme of homomorphic encryption is the CKKS scheme. CKKS is an approximate homomorphic encryption scheme that supports real and complex number operations. In this paper, we propose a method to efficiently compute support among evaluation metrics of association analysis using CKKS scheme, and a method to compute supports in parallel using matrix multiplication for multiple itemsets. We implemented and evaluated the proposed method to compute supports using the HEaaN library. According to evaluation results, the support value calculated by the proposed method was almost identical to that calculated without encryption, confirming that the proposed method could effectively calculate the support value while protecting user data privacy.