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Gradient Boosting Decision Trees (GBDTs) are widely used in industry and academia for their high accuracy and efficiency, particularly on structured data. However, the subject of watermarking GBDT models remains underexplored, especially compared to neural networks. In this work, we present the first robust watermarking framework tailored to GBDT models, utilizing in-place fine-tuning to embed imperceptible and resilient watermarks. We propose four embedding strategies, each designed to minimize impact on model accuracy while ensuring watermark robustness. Through experiments across diverse datasets, we demonstrate that our methods achieve high watermark embedding rates, low accuracy degradation, and strong resistance to post-deployment fine-tuning. 

Post-Quantum Cryptography (PQC) and Homomorphic Encryption (HE) are emerging security primitives that strengthen data protection against adversaries equipped with quantum computing capabilities. Although PQC and HE rely on similar underlying arithmetic operations, their hardware implementations are typically developed independently due to differences in key parameters such as polynomial length and modulus bit-width. This work presents a unified lattice-based polynomial modular accelerator that efficiently supports both PQC and HE primitives, bridging these two domains toward future secure computing architectures. The proposed design introduces highly reconfigurable modular computation units that enable low-overhead runtime configuration across the parameter ranges commonly used in PQC and HE schemes. This unified architecture eliminates the need for separate domain-specific accelerators by reusing shared computation structures and workload patterns across both cryptographic schemes. 

Homomorphic encryption enables computations on the ciphertext to preserve data privacy. However, its practical deployment has been hindered by the significant computational overhead compared to the plaintext computations. In response to this challenge, we present HERMES, a novel hardware acceleration system designed to explore the computation flow of the CKKS homomorphic encryption bootstrapping process. Among the major contributions of our proposed architecture, we first analyze the properties of the CKKS computation data flow and propose a new scheduling strategy by partitioning the computation modules into general-purpose and special-purpose modular computation modules to allow smaller resource consumption and flexible scheduling. The computation modules are also reconfigurable to reduce the memory access overhead during the intermediate computation. We also optimize the CKKS computation dataflow to improve the regularity with reduced control overhead. 

Fully Homomorphic Encryption (FHE) presents a paradigm-shifting framework for performing computations on encrypted data, offering revolutionary implications for privacy-preserving technologies. This paper introduces a novel hardware implementation of scheme switching between two leading FHE schemes targeting different computational needs, i.e., arithmetic HE scheme CKKS, and Boolean HE scheme FHEW. The proposed architecture facilitates dynamic switching between the schemes with improved throughput and latency compared to the software baseline. The proposed architecture computation modules support scheme switching operations involving coefficient conversion, modular switching, and key switching. We also optimize the hardware designs for the pre-processing and post-processing blocks, involving key generation, encryption, and decryption. The effectiveness of our proposed design is verified on the Xilinx U280 Datacenter Acceleration FPGA. We demonstrate that the proposed scheme switching accelerator yields a 365× performance improvement over the software counterpart.