Confidential Analytics with Scylla

While security concerns of data at rest and in transit have been addressed over the years using standard cryptographic measures, those surrounding data in use have garnered significant attention in recent times. In response, various trusted execution environments (TEEs) have been proposed and are on offer from leading public cloud providers. With development and re-programming efforts, availability, threat models, pricing, performance, etc., differing between various TEEs themselves and also with viable alternatives such as software solutions like partially homomorphic encryption (PHE) to protect data in use, it is imperative to have a system that is independent of these several varying dimensions while also efficiently achieving end-to-end confidentiality guarantees on data processing.We propose Scylla, a mechanism-agnostic confidential analytics framework, built on top of the popular Spark data analytics engine. Scylla utilizes a customizable combination of TEEs and PHE schemes to achieve end-to-end confidentiality guarantees with prime performance. Our evaluation shows that Scylla's query execution times are 1.91× faster than state-of-the-art system Opaque providing similar guarantees. Scylla's novel general architecture enables integrating latest TEEs such as AWS Nitro, AMD SEV-SNP, and Intel TDX with zero rebuilding efforts.