$ \mathsf{GPABE} $GPABE: GPU-Based Parallelization Framework for Attribute-Based Encryption Schemes

Attribute-based encryption (ABE) has emerged as a new paradigm for access control in cloud computing. However, despite the many promising features of ABE, its deployment in real-world systems is still limited, partially due to the expensive cost of its underlying mathematical operations, which often grow linearly with the size and complexity of the system's security policies. This becomes particularly challenging in data-intensive applications, where multiple users may simultaneously access and manipulate large volumes of data, resulting in high levels of concurrency and demand for computing resources, which are too heavy even for high-end servers. Further exacerbating the issues are the functionality and security requirements of a cloud, as they introduce additional computations to both the client and the server. Therefore, in this work, we introduce $ \mathsf{GPABE} $, the first GPU-based parallelization framework for ABE to facilitate its batch processing in cloud computing. By analyzing ABE's major computational workload, we identify multiple arithmetic modules that are common in the design of pairing-based ABEs. Based on the analysis, we further propose to decompose the ABE algorithm into computation graph, which can be efficiently implemented on the GPU platform. Our graph representation bridges the gap between ABE's high-level design and their low-level implementation on GPUs, and is applicable to a variety of popular schemes in the realm of ABE. We then implement $ \mathsf{GPABE} $ as a heterogeneous computing server, with several optimization techniques to improve its throughput. Finally, we evaluate the GPU implementation of several ABE schemes using $ \mathsf{GPABE} $. The results show a speedup of least 51.0× and at most 253.6× for the throughput of ABE algorithms, compared to their state-of-the-art CPU implementations, which preliminarily demonstrated the effectiveness of $ \mathsf{GPABE} $.