Processing-in-memory (PIM) architectures are promising for accelerating intensive workloads due to their high internal bandwidth. This paper introduces a technique for accelerating Fully Homomorphic Encryption over the Torus (TFHE), a promising yet intensive application, on a realistic PIM system. Existing TFHE accelerators focus on exploiting parallelism, often overlooking data affinity, which leads to performance degradation in PIM due to excessive remote data accesses (RDAs). To address this, we present an affinity-based approach that optimizes the computation of TFHE on PIM. We apply algorithmic optimizations to TFHE, enabling PIM to effectively leverage its high internal bandwidth. We analyze the affinity patterns in the sub-tasks of TFHE and develop an offline scheduler that exploits our analysis to find optimal scheduling, minimizing RDAs while maintaining sufficient parallelism. To demonstrate the practicality of our work, we design a variant of an existing PIM-HBM device with minimal hardware modifications, and perform evaluations over a real FPGA-based PIM system. Our experiments demonstrate that our affinity-based optimizations outperform prior TFHE accelerators by 4.24-209× for real-world benchmarks.

Graph Neural Networks (GNNs) are increasingly used in domains such as finance and bioinformatics, where both node features and edge structures can contain sensitive information. While Fully Homomorphic Encryption (FHE) offers a promising solution for privacy-preserving GNN inference, existing approaches such as PPGNN rely on costly Homomorphic Rotation and MUX operations for operand obfuscation, resulting in significant computational overhead. In this work, we propose a new obfuscation method that leverages the probabilistic nature of FHE to duplicate ciphertexts at the client side, thereby eliminating the need for runtime selection logic. To support this method efficiently, we design a pipelined hardware accelerator with a simplified CKKS datapath and parallel TFHE execution, avoiding the complexity of rotation-heavy designs. Despite reduced ciphertext reuse, our architecture mitigates memory pressure through buffer-aware PBS unit design. Experimental results demonstrate up to$8.8 \times$speedup and$7.69 \times$energy efficiency improvement over PPGNN, while also outperforming existing multi-scheme accelerators such as Trinity and UFC even when applying the same obfuscation strategy. Our approach offers a practical and scalable solution for efficient, privacy-preserving GNN inference.