Fully homomorphic encryption (FHE) is a powerful cryptographic technique that enables computation on encrypted data without needing to decrypt it. It has broad applications in scenarios where sensitive data needs to be processed in the cloud or in other untrusted environments. FHE applications are both compute- and memory-intensive, owing to expensive operations on large data. While prior works address the challenges of efficient compute using dedicated hardware, expensive memory transfers still remain a major limiting factor. In this work, we propose a hierarchical near-DRAM processing (NDP) solution for FHE applications, called FHENDI, that harnesses the massive DRAM bank bandwidth. We observe various data access patterns in FHE that reveal distinct levels of parallelism: element-wise, limb-wise, coefficient-wise, and ciphertext-wise. FHENDI exploits these levels of parallelism to map FHE operations and data onto different hierarchies of our design, while addressing three major challenges with NDP for FHE: (i) the lack of bank-to-bank communication support, (ii) limited die-to-die bandwidth, and (iii) large memory access latencies. We resolve the first problem through a novel, conflict-free mapping algorithm built atop localized permutation networks that enables efficient element-wise and butterfly operations in FHE. The second problem is addressed by pipelining the execution of parallel bootstrap operations observed in compiled FHE workloads. Finally, we hide the memory access latency behind computation latency by exploiting a dual-banking scheme and subarray-level parallelism (SLP) of the DRAM banks. We evaluate FHENDI using representative workloads in the domains of privacy-preserving machine learning inference on CNNs and Transformers, database range query, and sorting, that are obtained using a compiler framework called HElayers. We compare FHENDI with a server-class CPU and GPU running the state-of-the-art HEaaN library, and an FHE accelerator ASIC, and report mean speedups of $2145.8 \times, 118.29 \times$, and $2.45 \times$, respectively.

Designing privacy-preserving DL solutions is a major challenge within the AI community. Homomorphic Encryption (HE) has emerged as one of the most promising approaches in this realm, enabling the decoupling of knowledge between a model owner and a data owner. Despite extensive research and application of this technology, primarily in CNNs, applying HE on transformer models has been challenging because of the difficulties in converting these models into a polynomial form. We break new ground by introducing the first polynomial transformer, providing the first demonstration of secure inference over HE with full transformers. This includes a transformer architecture tailored for HE, alongside a novel method for converting operators to their polynomial equivalent. This innovation enables us to perform secure inference on LMs and ViTs with several datasts and tasks. Our techniques yield results comparable to traditional models, bridging the performance gap with transformers of similar scale and underscoring the viability of HE for state-of-the-art applications. Finally, we assess the stability of our models and conduct a series of ablations to quantify the contribution of each model component. Our code is publicly available.

Computation delegation to untrusted third-party while maintaining data confidentiality is possible with homomorphic encryption (HE). However, in many cases, the data was encrypted using another cryptographic scheme such as AES-GCM. Hybrid encryption (a.k.a Transciphering) is a technique that allows moving between cryptosystems, which currently has two main drawbacks: 1) lack of standardization or bad performance of symmetric decryption under FHE; 2) lack of input data integrity.

Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an inference operation over an encrypted HE-friendly AlexNet neural network with large inputs, which runs in around five minutes, several orders of magnitude faster than other state-of-the-art non-interactive HE solutions.