Searchable symmetric encryption (SSE) is a special class of encryption schemes for computing directly over encrypted data. SSE aims to be significantly more efficient as compared to other solutions, such as fully homomorphic encryption (FHE), while leaking only minimal information to the adversary. SSE is particularly efficient and scalable for Boolean queries over large encrypted relational databases outsourced to third-party cloud service providers. However, practical implementations of SSE often suffer from performance bottlenecks due to randomised memory accesses for reads/writes and computation-intensive cryptographic operations. As a result, a gap exists today between theoretically efficient SSE algorithms and practically efficient SSE systems for real-world databases. In this paper, we address this longstanding open question that has otherwise hindered the widespread deployment of SSE over real cloud computing platforms. We propose CAMiSE –a fully associative memory-integrated framework for designing SSE systems with fast query processing over extremely large databases. We show a novel usage of custom-designed Content Addressable Memory ( CAM ), together with robust data access policies, to bridge the memory wall in traditional SSE implementations by minimising storage-access latencies due to randomised look-up operations during searches. Coupled with dedicated hardware accelerators for cryptographic operations, CAMiSE achieves extremely fast and scalable query processing over encrypted relational databases. We prototype multiple well-known SSE algorithms and SSE data structures within our proposed CAMiSE framework. Our experiments show that these implementations achieve around $5\times $ to $7\times $ speed-up over traditional software-based implementations while scaling smoothly to extensive real-world databases with millions of records.

中文翻译：

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CAMiSE：内容可寻址内存集成可搜索加密

可搜索对称加密 (SSE) 是一类特殊的加密方案，用于直接对加密数据进行计算。SSE 的目标是比完全同态加密 (FHE) 等其他解决方案更加高效，同时只向对手泄露最少的信息。对于外包给第三方云服务提供商的大型加密关系数据库的布尔查询，SSE 特别高效且可扩展。然而，由于读/写和计算密集型加密操作的随机内存访问，SSE 的实际实现经常遇到性能瓶颈。因此，目前理论上有效的 SSE 算法与现实世界数据库的实际高效 SSE 系统之间存在差距。在本文中，我们解决了这个长期悬而未决的问题，否则该问题会阻碍 SSE 在真正的云计算平台上的广泛部署。我们建议CAMiSE – 一个完全关联的内存集成框架，用于设计 SSE 系统，在超大型数据库上进行快速查询处理。我们展示了定制设计的内容可寻址内存的新颖用法（ CAM ）与强大的数据访问策略一起，通过最大限度地减少搜索过程中随机查找操作导致的存储访问延迟，来弥合传统 SSE 实现中的内存墙。加上用于加密操作的专用硬件加速器，CAMiSE 在加密关系数据库上实现极快且可扩展的查询处理。我们在我们提出的方案中对多个著名的 SSE 算法和 SSE 数据结构进行了原型设计CAMiSE 框架。我们的实验表明这些实现实现了大约 $5\次$到 $7\次$比传统的基于软件的实施速度更快，同时平滑扩展到具有数百万条记录的广泛的现实世界数据库。