Design & Development of a Trusted Database Prototype to Execute SQL Queries with Privacy and Compliance

Abstract

Traditionally, as soon as confidentiality becomes a concern, data is encrypted before outsourcing to a service provider. Any software-based cryptographic constructs then deployed, for server-side query processing on the encrypted data, inherently limit query expressiveness. Here, we introduce Trusted DB, an outsourced database prototype that allows clients to execute SQL queries with privacy and under regulatory compliance constraints by leveraging server-hosted, tamper-proof trusted hardware in critical query processing stages, thereby removing any limitations on the type of supported queries. Despite the cost overhead and performance limitations of trusted hardware, we show that the costs per query are orders of magnitude lower than any existing or potential future software-only mechanisms. Trusted DB is built and runs on actual hardware and its performance and costs are evaluated here.
KEY WORDS: Classification and Regression Trees (CART); Chi Square Automatic Interaction Detection (CHAID); Content Addressable Memory(CAM); Conscious Space Saving with Stream Summary (CSSSS); Genetic Algorithms; Artificial Neural Networks; Rule Induction