EinHops: Einsum Notation for Expressive Homomorphic Operations on RNS-CKKS Tensors

Fully Homomorphic Encryption (FHE) is an encryption scheme that allows for computation to be performed directly on encrypted data, effectively closing the loop on secure and outsourced computing. Data is encrypted not only during rest and transit, but also during processing. However, FHE provides a limited instruction set: SIMD addition, SIMD multiplication, and cyclic rotation of 1-D vectors. This restriction makes performing multi-dimensional tensor operations challenging. Practitioners must pack these tensors into 1-D vectors and map tensor operations onto this one-dimensional layout rather than their traditional nested structure. And while prior systems have made significant strides in automating this process, they often hide critical packing decisions behind layers of abstraction, making debugging, optimizing, and building on top of these systems difficult.

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Publication Details

Published
2025-09-22
DOI
https://doi.org/10.1145/3733811.3767309
Primary Topic
Bacterial Genetics and Biotechnology
Type
article
Field-Weighted Citation Impact
0.00
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article

EinHops: Einsum Notation for Expressive Homomorphic Operations on RNS-CKKS Tensors

Bacterial Genetics and Biotechnology
article

EinHops: Einsum Notation for Expressive Homomorphic Operations on RNS-CKKS Tensors

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Abstract

Fully Homomorphic Encryption (FHE) is an encryption scheme that allows for computation to be performed directly on encrypted data, effectively closing the loop on secure and outsourced computing. Data is encrypted not only during rest and transit, but also during processing. However, FHE provides a limited instruction set: SIMD addition, SIMD multiplication, and cyclic rotation of 1-D vectors. This restriction makes performing multi-dimensional tensor operations challenging. Practitioners must pack these tensors into 1-D vectors and map tensor operations onto this one-dimensional layout rather than their traditional nested structure. And while prior systems have made significant strides in automating this process, they often hide critical packing decisions behind layers of abstraction, making debugging, optimizing, and building on top of these systems difficult.

New York University (US)
Openalex Percentile: Top 37%
Bacterial Genetics and Biotechnology
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