SecuDB: An In-enclave Privacy-preserving and Tamper-resistant Relational Database

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Abstract

With the escalation in the demand for privacy-preserving and tamper-resistant data management and processing on the public cloud, an increasing number of mainstream databases start to provide always-encrypted and blockchain-like features, including Microsoft SQL Server, MongoDB, and Alibaba PolarDB. The recent progress in Trusted Execution Environment (TEE) technology has enabled the deployment of the complete database engine within TEE. This implementation ensures that data stored in memory, cache, and registers is encrypted, thereby maintaining the confidentiality of information. In this paper, we present SecuDB, a multi-granularity privacy-preserving and tamper-resistant relational database by placing the entire RDBMS in Intel TDX. We propose a novel visibility control mechanism incorporating column masking, log masking, and statistics masking to realize fine-grained privacy preservation and devise an isolated TEE-endorsed temporal table method to support efficient data and query verifiability, without affecting insertion and selection performance. We evaluate SecuDB using Sysbench, TPC-C and TikTok copyright workloads. The results show that compared with a system without an enclave, SecuDB hits 84.7% and 94.7% of the performance when providing coarse-grained and fine-grained privacy preservation, respectively. While the overhead for tamper-resistance is less than 22.6%.