HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption | Paper Citation Record

Observation d4052248-b8b8-45a2-8a86-55ea3c315c20 · outbound

This paper cites Fab: An fpga- based accelerator for bootstrappable fully homomorphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Fab: An fpga- based accelerator for bootstrappable fully homomorphic encryption

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:23.254171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5925ef21-83e1-42f4-8804-49f93704123a · outbound

This paper cites HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:49.094747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33e88d88-9947-4300-bc25-72260b2aaede · outbound

This paper cites Llama 3 model card.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Llama 3 model card

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:22.934195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f5b0dab-448e-43e2-8afd-2fee477655ab · outbound

This paper cites Privft: Private and fast text classification with homomorphic encryption.IEEE Access, 8:226544–226556, 2020.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Privft: Private and fast text classification with homomorphic encryption.IEEE Access, 8:226544–226556, 2020

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:22.612384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c721210-936a-4e8e-b932-5f27f3a10bb6 · outbound

This paper cites TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:49.564750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1772cc59-7e5d-44cc-af92-d58141556f99 · outbound

This paper cites ngraph-he2: A high- throughput framework for neural network inference on encrypted data.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption ngraph-he2: A high- throughput framework for neural network inference on encrypted data

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:22.269835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7f7c877-4e73-498c-a7df-1dd1c44329ad · outbound

This paper cites ngraph-he: a graph compiler for deep learning on homomorphically encrypted data.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption ngraph-he: a graph compiler for deep learning on homomorphically encrypted data

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:21.947723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f87cbc5-4dec-40a6-9dba-a478caecaadf · outbound

This paper cites Efficient bootstrapping for approximate homomorphic encryption with non-sparse keys.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Efficient bootstrapping for approximate homomorphic encryption with non-sparse keys

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:21.582166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a712a14b-f3e3-43e7-9f1d-78b80d5e3dbc · outbound

This paper cites Fast homomorphic evaluation of deep discretized neural networks.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Fast homomorphic evaluation of deep discretized neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:21.303098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f8878e7-047c-4ea0-9cae-529ca10f051f · outbound

This paper cites THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:50.462610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2953b95d-08cf-4e22-9888-2a1a876170c5 · outbound

This paper cites Bootstrapping for approxi- mate homomorphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Bootstrapping for approxi- mate homomorphic encryption

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:20.956308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3519b341-0c29-40f1-99fb-c072d3252748 · outbound

This paper cites Homomorphic encryption for arithmetic of approximate numbers.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Homomorphic encryption for arithmetic of approximate numbers

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:20.560152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ba4fe332-1261-46ab-82b9-d1b70727d522 · outbound

This paper cites {DaCapo}: Automatic boot- strapping management for efficient fully homomorphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption {DaCapo}: Automatic boot- strapping management for efficient fully homomorphic encryption

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:20.230864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a6d6f250-98de-43ea-9054-e2b3532d7315 · outbound

This paper cites Ched- dar: A swift fully homomorphic encryption library de- signed for gpu architectures.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Ched- dar: A swift fully homomorphic encryption library de- signed for gpu architectures

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:19.862178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bc88a735-e159-4a59-8a06-ebe74c05846c · outbound

This paper cites Por- cupine: A synthesizing compiler for vectorized homo- morphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Por- cupine: A synthesizing compiler for vectorized homo- morphic encryption

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:19.544907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3c70f5c-53d7-4b31-a480-6900bae7188c · outbound

This paper cites Criteo display advertising chal- lenge dataset.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Criteo display advertising chal- lenge dataset

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:19.263002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5be59d18-bc0d-46ef-8b9b-b763f144da1a · outbound

This paper cites cuHE: CUDA homomorphic encryption library.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption cuHE: CUDA homomorphic encryption library

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:18.972894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 085eebdf-54c0-4835-bc37-e4b3456c4d6a · outbound

This paper cites Eva: An encrypted vector arithmetic language and compiler for efficient homomorphic computation.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Eva: An encrypted vector arithmetic language and compiler for efficient homomorphic computation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:18.503087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95732404-1a69-4d83-a72b-260bab81c5a1 · outbound

This paper cites Chet: an optimizing compiler for fully-homomorphic neural-network inferencing.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Chet: an optimizing compiler for fully-homomorphic neural-network inferencing

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:18.245208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dbdfd408-8b88-481b-9ff6-5818b73e8b95 · outbound

This paper cites Low-precision hardware architectures meet recommendation model in- ference at scale.IEEE Micro, 41(5):93–100, 2021.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Low-precision hardware architectures meet recommendation model in- ference at scale.IEEE Micro, 41(5):93–100, 2021

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:17.913508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2642eaa-84b1-4a08-949b-b5f5a842e673 · outbound

This paper cites A unified vector processing unit for fully homomorphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption A unified vector processing unit for fully homomorphic encryption

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:52.615271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ba9a284-6aed-453f-a6fd-21d00421b034 · outbound

This paper cites Orion: A fully homomorphic encryption framework for deep learning.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Orion: A fully homomorphic encryption framework for deep learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:52.794865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57b29ce2-d935-4edc-be7b-1237583b09d2 · outbound

This paper cites Osiris: A systolic approach to accelerating fully homomorphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Osiris: A systolic approach to accelerating fully homomorphic encryption

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:17.575912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 686e318d-0ebe-4d45-8b48-0a057cedc5ef · outbound

This paper cites A pragmatic introduction to secure multi-party com- putation.Found.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption A pragmatic introduction to secure multi-party com- putation.Found

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:53.244858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10033372-1d56-44bc-b5a5-ab1c7519ad2d · outbound

This paper cites F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version).

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version)

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:29:05.704742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09b9926e-cfb4-4a5e-9a71-52a686352fa5 · outbound

This paper cites Fully homomorphic encryption using ideal lattices.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Fully homomorphic encryption using ideal lattices

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:53.720924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16ae08c8-23b7-4d00-a9ac-702c8509ff60 · outbound

This paper cites Learning to Collide: Recommendation System Model Compression with Learned Hash Functions.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Learning to Collide: Recommendation System Model Compression with Learned Hash Functions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:53.904925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35dcc6ed-0b3f-4107-947b-beb19413eba7 · outbound

This paper cites Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:54.164875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1261428b-a3dd-4b4c-ac5b-b346e9c2fda4 · outbound

This paper cites Post-Training 4-bit Quantization on Embedding Tables.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Post-Training 4-bit Quantization on Embedding Tables

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:54.354254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caf12507-7a06-47e2-8b3c-33840e02dfc3 · outbound

This paper cites The Architectural Implications of Facebook's DNN-based Personalized Recommendation.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption The Architectural Implications of Facebook's DNN-based Personalized Recommendation

Reference 30

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T23:29:05.094748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4be4972-50d1-483e-a5b3-1bd8c4e4523f · outbound

This paper cites CipherGPT: Secure two-party GPT inference.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption CipherGPT: Secure two-party GPT inference

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:17.181472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2cd39720-97cd-484b-9e1b-c000f16f0ca2 · outbound

This paper cites Cheetah: Lean and fast secure Two-Party deep neural network inference.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Cheetah: Lean and fast secure Two-Party deep neural network inference

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:16.806499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fb636029-c2e3-4480-bca4-4e2191746074 · outbound

This paper cites tiktoken: A fast bpe to- keniser for use with openai’s models.https://github.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption tiktoken: A fast bpe to- keniser for use with openai’s models.https://github

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:16.554743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dad4e68a-c7f1-4165-ba73-b04036857259 · outbound

This paper cites Heart Disease.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Heart Disease

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:55.584826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aac9ef1-b106-435b-9f9c-2884a6f426aa · outbound

This paper cites an unresolved cited work.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:29:16.070487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ea83241d-6b40-4a33-a041-ce68a848c638 · outbound

This paper cites GAZELLE: A low latency framework for secure neural network inference.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption GAZELLE: A low latency framework for secure neural network inference

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:15.600920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3bdaa6a8-3144-4bdc-8fba-b3583136098b · outbound

This paper cites Learning multi-granular quantized embeddings for large- vocab categorical features in recommender systems.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Learning multi-granular quantized embeddings for large- vocab categorical features in recommender systems

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:15.284770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 83cdf5ca-adf9-4a40-a88b-e9b3dce29b49 · outbound

This paper cites Sharp: A short-word hierarchical accelerator for robust and prac- tical fully homomorphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Sharp: A short-word hierarchical accelerator for robust and prac- tical fully homomorphic encryption

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:14.802342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09f3c397-3e82-4ccc-99a2-3c1e12f74d8f · outbound

This paper cites ARK: Fully homomorphic encryption accelerator with runtime data generation and inter-operation key reuse.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption ARK: Fully homomorphic encryption accelerator with runtime data generation and inter-operation key reuse

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:56.574864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1bbbadd-e630-40a6-a124-ef35d5710880 · outbound

This paper cites an unresolved cited work.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:29:14.554755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46966d2d-f194-4093-9d38-6a0e8bfa3519 · outbound

This paper cites Mascot: A quantization framework for efficient matrix factorization in recommender systems.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Mascot: A quantization framework for efficient matrix factorization in recommender systems

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:14.014577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 848f6d67-62b5-4f54-89bb-39d3cfff1b0a · outbound

This paper cites A tensor compiler with automatic data packing for simple and efficient fully homomorphic encryption.Proceedings of the ACM on Programming Languages, 8(PLDI):126– 150, 2024.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption A tensor compiler with automatic data packing for simple and efficient fully homomorphic encryption.Proceedings of the ACM on Programming Languages, 8(PLDI):126– 150, 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:13.694919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3a3f255a-c643-49de-a757-61aa52864fa4 · outbound

This paper cites Low-complexity deep convolutional neural networks on fully homomorphic encryption using multiplexed parallel convolutions.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Low-complexity deep convolutional neural networks on fully homomorphic encryption using multiplexed parallel convolutions

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:13.403551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c661e03-4ef5-4e89-ae75-7ab98f1bb3d8 · outbound

This paper cites Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:29:04.461364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2a07826-0d96-42bd-ab0f-5137c986add5 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:57.664738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e76b3105-7666-436a-8053-57508db4a93d · outbound

This paper cites {ELASM}:{Error- Latency-Aware} scale management for fully homomor- phic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption {ELASM}:{Error- Latency-Aware} scale management for fully homomor- phic encryption

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:13.084989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ed798e9-367f-4b75-9cf7-a58d5f346881 · outbound

This paper cites Hecate: Performance-aware scale optimization for homomorphic encryption compiler.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Hecate: Performance-aware scale optimization for homomorphic encryption compiler

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:12.814780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a4557f4b-09fa-40b5-861b-36adcae95400 · outbound

This paper cites Fastquery: Communication- efficient embedding table query for private llm inference,.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Fastquery: Communication- efficient embedding table query for private llm inference,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:12.524749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 613450b1-3035-40c8-becc-e2e21ab9a8ed · outbound

This paper cites Oblivious neural network predictions via minionn trans- formations.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Oblivious neural network predictions via minionn trans- formations

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:12.331112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 730d8d2a-ee84-418a-ac79-55aa995ce5c6 · outbound

This paper cites Cafe+: Towards compact, adaptive, and fast embedding for large-scale online recommendation models.ACM Transactions on Information Systems, 2025.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Cafe+: Towards compact, adaptive, and fast embedding for large-scale online recommendation models.ACM Transactions on Information Systems, 2025

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:11.904765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbb8fd83-14fc-4428-8a63-9b5206c33f2b · outbound

This paper cites Coy- ote: A compiler for vectorizing encrypted arithmetic circuits.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Coy- ote: A compiler for vectorizing encrypted arithmetic circuits

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:11.495104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 77e81082-97e9-4f4c-83bb-88422f3a026c · outbound

This paper cites Thor: Secure transformer inference with homo- morphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Thor: Secure transformer inference with homo- morphic encryption

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:11.017967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c4722516-ccee-4d85-9aad-a6c14b36983a · outbound

This paper cites Deep Learning Recommendation Model for Personalization and Recommendation Systems.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:58.999330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b2a2aa5-9676-4517-a4e8-bbac1b4d3d17 · outbound

This paper cites Language models are un- supervised multitask learners.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Language models are un- supervised multitask learners

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:10.884223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4463b2c-f724-4711-bc58-539fbc32ee6b · outbound

This paper cites Cheetah: Optimizing and accelerating homo- morphic encryption for private inference.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Cheetah: Optimizing and accelerating homo- morphic encryption for private inference

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:10.597494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 53517a1a-b478-4910-a692-0682852d9615 · outbound

This paper cites Sadegh Riazi, Kim Laine, Blake Pelton, and Wei Dai.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Sadegh Riazi, Kim Laine, Blake Pelton, and Wei Dai

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:59.472454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab4b096b-b09f-4b58-b242-6a8b44319bf9 · outbound

This paper cites Craterlake: A hardware accelerator for efficient un- bounded computation on encrypted data.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Craterlake: A hardware accelerator for efficient un- bounded computation on encrypted data

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:59.569249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe5fae30-f61a-4613-9417-84eacdf8cff3 · outbound

This paper cites Compositional embeddings using complementary partitions for memory-efficient recom- mendation systems.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Compositional embeddings using complementary partitions for memory-efficient recom- mendation systems

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:10.334557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 858040b9-453a-4d35-b89e-98a19ecc8152 · outbound

This paper cites Compressing word embeddings via deep compositional code learn- ing.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Compressing word embeddings via deep compositional code learn- ing

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:09.999906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 26d041c1-6bbf-4a5c-a755-a061acfcd27a · outbound

This paper cites Delphi: A cryptographic inference service for neural networks.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Delphi: A cryptographic inference service for neural networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:09.677324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 137b20f9-6550-4829-8a9d-6312b233edc4 · outbound

This paper cites Clustering the sketch: dynamic compression for embedding tables.Advances in Neural Information Processing Systems, 36:72155– 72180, 2023.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Clustering the sketch: dynamic compression for embedding tables.Advances in Neural Information Processing Systems, 36:72155– 72180, 2023

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:09.374755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b43fd4ec-474a-4613-a17a-3d58b23473e3 · outbound

This paper cites SEALion: a Framework for Neural Network Inference on Encrypted Data.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption SEALion: a Framework for Neural Network Inference on Encrypted Data

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T23:29:00.678622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dfd9da6-cff3-4545-9aa9-ea9ac27be986 · outbound

This paper cites Heco: fully homomorphic encryption com- piler.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Heco: fully homomorphic encryption com- piler

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:09.065078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f71bf60a-dc75-41e7-8f57-e8e48b4723f3 · outbound

This paper cites Feature hashing for large scale multitask learning.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Feature hashing for large scale multitask learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:08.835858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f4f484c8-48db-425a-ba0b-0fdc7e445315 · outbound

This paper cites Arion: Attention- optimized transformer inference on encrypted data.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Arion: Attention- optimized transformer inference on encrypted data

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:08.512160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a6fc6916-2b44-468a-a384-dcc7d6ab0efb · outbound

This paper cites Poseidon: Prac- tical homomorphic encryption accelerator.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Poseidon: Prac- tical homomorphic encryption accelerator

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T23:29:01.254425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 789ae3a4-ce1e-4262-8666-3e0f02f51c41 · outbound

This paper cites Tt-rec: Tensor train compression for deep learn- ing recommendation models.Proceedings of Machine Learning and Systems, 3:448–462, 2021.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Tt-rec: Tensor train compression for deep learn- ing recommendation models.Proceedings of Machine Learning and Systems, 3:448–462, 2021

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:07.954762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9fa2ae19-a887-4155-9938-0e232a388974 · outbound

This paper cites Privacy-preserving embedding via look-up ta- ble evaluation with fully homomorphic encryption.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Privacy-preserving embedding via look-up ta- ble evaluation with fully homomorphic encryption

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:07.558345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 25936485-6426-455b-aad5-0858e6ace40c · outbound

This paper cites Concrete ML: a privacy-preserving machine learning library using fully homomorphic encryption for data scientists, 2022.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Concrete ML: a privacy-preserving machine learning library using fully homomorphic encryption for data scientists, 2022

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:07.224751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b86f9229-43ca-4d37-8cb3-b034dd05088a · outbound

This paper cites Secure transformer infer- ence made non-interactive.Cryptology ePrint Archive, 2024.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption Secure transformer infer- ence made non-interactive.Cryptology ePrint Archive, 2024

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:06.893086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d5eec759-c3f0-4318-8987-95a2d425a161 · outbound

This paper cites MOAI: Module-optimizing architec- ture for non-interactive secure transformer inference.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption MOAI: Module-optimizing architec- ture for non-interactive secure transformer inference

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:29:06.594918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66d67b77-085a-4970-976d-03dcc033847c · outbound

This paper cites DQRM: Deep Quantized Recommendation Models.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption DQRM: Deep Quantized Recommendation Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T23:29:02.024766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5cb7f4a-5432-4da9-955e-d4518d9fed1d · outbound

This paper cites 3527415,doi:10.1145/3470496.3527415.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption 3527415,doi:10.1145/3470496.3527415

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:57.069195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c72c154-679e-4306-a808-b1467e99f57c · outbound

This paper cites FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference.

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:29:03.834822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers