Announcing the Final Examination of Ardhi Wiratama Baskara Yudha for the degree of Doctor of Philosophy
Cloud computing increasingly handles confidential data, like private inference and query databases. Two strategies are
used for secure computation: (1) employing CPU Trusted Execution Environments (TEEs) like AMD SEV, Intel SGX, or
ARM TrustZone, and (2) utilizing emerging cryptographic methods like Fully Homomorphic Encryption (FHE) with libraries
such as HElib, Microsoft SEAL, and PALISADE. To enhance computation, GPUs are often employed. However, using
GPUs to accelerate secure computation introduces challenges addressed in two works. In the first work, we tackle GPU
acceleration for secure computation with CPU TEEs. While TEEs
perform computations on confidential data, extending their capabilities to GPUs is essential for leveraging their power.
Existing approaches assume co-designed CPU-GPU setups, but we contend that co-designing CPU and GPU is difficult to
achieve and requires early coordination between CPU and GPU manufacturers. To address this, we propose
software-based memory encryption for CPU-GPU TEE co-design via the software layer. Yet, this introduces issues due to
AES's 128-bit granularity. We present optimizations to mitigate these problems, resulting in execution time overheads of
1.1% and 56% for regular and irregular applications. In the second work, we focus on GPU acceleration for the HElib,
particularly for comparison operations on encrypted data. These operations are vital in Machine Learning, Image
Processing, and Private Database Queries, yet their acceleration is often overlooked. We extend HElib to harness GPU
acceleration for its resource-intensive components like BluesteinNTT, BluesteinFFT, and Element-wise Operations.
Addressing memory separation, dynamic allocation, and parallelization challenges, we employ several optimizations to
address these challenges. With all optimizations and hybrid CPU-GPU parallelism, we achieve a 5.4× average speedup
over the state-of-the-art CPU library. Overcoming these challenges is crucial for achieving significant GPU-driven
performance improvements. This dissertation provides solutions to these hurdles, aiming to facilitate GPU-based
acceleration of confidential data computation.