🤖 AI Summary
This work addresses the challenge of reconciling fine-grained access control with high-performance encryption in pre-release large-scale astronomical image catalogs. The authors propose a novel framework that integrates a flexible policy engine with GPU-accelerated AES-GCM authenticated encryption, introducing parallel tree reduction into GHASH computation for the first time. This innovation transforms the traditionally sequential authentication hash process into a logarithmic-time parallel operation, substantially enhancing encryption throughput for petabyte-scale astronomical datasets. The approach ensures both confidentiality and integrity while enabling efficient and secure transition toward FAIR-compliant public archives.
📝 Abstract
The era of large astronomical surveys generates massive image catalogs requiring efficient and secure access, particularly during pre-publication periods where data confidentiality and integrity are paramount. While Findable, Accessible, Interoperable, and Reusable (FAIR) principles guide the eventual public dissemination of data, traditional security methods for restricted phases often lack granularity or incur prohibitive performance penalties. To address this, we present a framework that integrates a flexible policy engine for fine-grained access control with a novel GPU-accelerated implementation of the AES-GCM authenticated encryption protocol.
The novelty of this work lies in the adaptation and optimization of a parallel tree-reduction strategy to overcome the main performance bottleneck in authenticated encryption on GPUs: the inherently sequential Galois/Counter Mode (GCM) authentication hash (GHASH). We present both the algorithmic adaptation and its efficient execution on GPU architectures. Although similar parallelization techniques have been explored in cryptographic research, this is, to our knowledge, the first demonstration of their integration into a high-throughput encryption framework specifically designed for large-scale astronomical data. Our implementation transforms the sequential GHASH computation into a highly parallelizable, logarithmic-time process, achieving authenticated encryption throughput suitable for petabyte-scale image analysis.
Our solution provides a robust mechanism for data providers to enforce access policies, ensuring both confidentiality and integrity without hindering research workflows, thereby facilitating a secure and managed transition of data to public, FAIR archives.