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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.
Optimizing Homomorphic Compiler HedgeHog for DNN Model based on CKKS Homomorphic Encryption Scheme
Dongkwon Lee, Gyejin Lee, Suchan Kim, Woosung Song, Dohyung Lee, Hoon Kim, Seunghan Jo, Kyuyeon Park, Kwangkeun Yi
http://doi.org/10.5626/JOK.2022.49.9.743
We present a new state-of-the-art optimizing homomorphic compiler HedgeHog based on high-level input language. Although homomorphic encryption enables safe and secure third party computation, it is hard to build high-performance HE applications without expertise. Homomorphic compiler lowers this hurdle, but most of the existing compilers are based on HE scheme that does not support real number computation and a few compilers based on the CKKS HE scheme that supports real number computation uses low-level input language. We present an optimizing compiler HedgeHog whose input language supports high-level DNN operators. In addition to its ease of use, compiled HE code shows a maximum of 22% more of speedup than the existing state-of-the-art compiler.
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.
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