Publications
カンファレンス (国際) Construction of Differentially Private Summaries over Fully Homomorphic Encryption
Shojiro Ushiyama (Waseda University), Tsubasa Takahashi, Masashi Kudo (Waseda University), Hayato Yamana (Waseda University)
The 32nd International Conference on Database and Expert Systems Applications (DEXA 2021)
2021.9.27
Cloud computing has garnered attention as a platform of query processing systems. However, data privacy leakage is a critical problem. Chowdhury et al. proposed Cryptε, which executes differential privacy (DP) over encrypted data on two non-colluding semi-honest servers. Further, the DP index proposed by these authors summarizes a dataset to prevent information leakage while improving the performance. However, two problems persist: 1) the original data are decrypted to apply sorting via a garbled circuit, and 2) the added noise becomes large because the sorted data are partitioned with equal width, regardless of the data distribution. To solve these problems, we propose a new method called DP-summary that summarizes a dataset into differentially private data over a homomorphic encryption without decryption, thereby enhancing data security. Furthermore, our scheme adopts Li et al.’s data-aware and workload-aware (DAWA) algorithm for the encrypted data, thereby minimizing the noise caused by DP and reducing the errors of query responses. An experimental evaluation using torus fully homomorphic encryption (TFHE), a bit-wise fully homomorphic encryption library, confirms the applicability of the proposed method, which summarized eight 16-bit data in 12.5 h. We also confirmed that there was no accuracy degradation even after adopting TFHE along with the DAWA algorithm.
Paper :
Construction of Differentially Private Summaries over Fully Homomorphic Encryption
（外部サイト）