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Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 1 inbound Pith citation observation for arXiv:2506.18150.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.18150 v4

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:29:02.024766Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:16:58.039873Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T11:16:58.067785Z

Reference resolution

74 of 74 outbound references displayed

  • verified exact3
  • verified fuzzy48
  • unresolved22
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:48.876803Z digest=sha256:5bac6b4bf8720734e92e9f8aa648cefea6c1fcb05d6fad8753b81c5d710ee84c

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:49.094747Z digest=sha256:c983fdcaeb3d279c1ef4a17a458b1fbb0a73b30327d9cda3270c478d673a6a1b

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:49.224745Z digest=sha256:ceb158e0e8fa6abb2b3fe8b117a20900ad33ba4eae059ca04875f615a67bffe7

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:49.376877Z digest=sha256:0d6082e256860b6f95cc9a9dcfd19b1159d1cca541fd820c86c266edbecc4ba5

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:49.564750Z digest=sha256:811f809a817f075b38f72832b903fab4e11d18e490bca68c390738b7346aa596

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:49.694755Z digest=sha256:74f8897961babe2d00594497721d4f92ec50f535569107b7a02d45f510cf4a77

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:49.807033Z digest=sha256:74177b0cc4967858f03ae713ade5b74be07601f8de0e5004713a4c99573463d9

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:50.034742Z digest=sha256:0186a5bdc3bc4280020a37e1077907992f2ccedd2038783bc43db9c1403b0291

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:50.213669Z digest=sha256:6bccc405383d629a46e390ed59e0568e13e49d66786e69a3c25be62084a202ef

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:50.462610Z digest=sha256:075667a9666071f95949863e79d2be4e6f35e37a7be2de7aab9c25d210f2a3f3

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:50.644845Z digest=sha256:f4742c6f1de5b6854e3f566b6720f169d41557d91be1fe807b969d3bbb144bfa

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:50.811888Z digest=sha256:698da8cf2063192f9559fcf42f9a5e6c56f25370fbd9bfa1d66ea29e05e95193

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:50.980049Z digest=sha256:869f28759ec54814ea10e92e48ce9494a5be730af587c130d9e2b2183b03480c

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:51.160549Z digest=sha256:83144732d809fe169baaf2002f397633e1ae66bff736643dc51b7d6922e4fbf6

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:51.355054Z digest=sha256:f02482cb81a2a139b494df55d832ed8eb94d2336a6a40e875e289cb700e65a29

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:51.564747Z digest=sha256:54f038d4a37f4549d3aed0d846b2a451a14b571dfc984c1488d86380f657b900

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:51.830176Z digest=sha256:8732d228377900e3407f928b8f7cb83e5c18b9940727c9359fc1e95201983623

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:52.054754Z digest=sha256:515a4ac83f1bf68eabf24b1aec303f067e77a3ba5689de476a3a1cf96a770bee

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:52.233742Z digest=sha256:c0f587d54dc12f526f9f0fdb632598d2982a6eb594fa54ea7de8ac189ae35dc1

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:52.439242Z digest=sha256:14940d93d842828d8980a2f8c75b8fddeaa013ec568e90b8ac7b408f3eed8f92

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:52.615271Z digest=sha256:016d8f29e173ab7a7a94211b241f05a44331fdbe2a1de746024cd9ab49567dd4

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:52.794865Z digest=sha256:7a585da20ab64b50aacf6ae1ebc9197c97b1d38c9b4c550990f8b3357f475328

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:52.994974Z digest=sha256:4cde883462f0d2d9fca64c379781ea05a4b30bf73e592a7adee1ecff783b3c06

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:53.244858Z digest=sha256:0e78c803263427b71ea2bdf4d6e7dee9eebcc786d58d3849fd071836da8fdd01

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:53.466825Z digest=sha256:6d650e5ff3d245bc8a49ba202f7914c8e3bbd903790dc39bf0fbe418d5459f34

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:53.720924Z digest=sha256:4d311fac9adb89ba57573c67b46c2b1f94dca57efc8bc2cce1e10bffdeb7750e

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:53.904925Z digest=sha256:65e4770a01a84bd604b1b114ed871dd94c7f97f576b11d6d768ec4f30e365ee2

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:54.164875Z digest=sha256:700972c35b17b1eedc445f8e8736655a99ae6c5640459d6f2b979ca896343444

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:54.354254Z digest=sha256:c106ad31a7c71300013cc46e07abbd315cc1472d0aacdfcaf00ad5364cca6f29

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:54.561970Z digest=sha256:5b9a59c7c90186436c4f7eac4157150eecb4de7f9c7b340c9b02b29f45fcdab9

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:54.764801Z digest=sha256:0dd3d22446591efc319a99c0e7d512449513847584e3eff4f772fec1fa45c3e4

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:54.946236Z digest=sha256:e7c5b681f6cdee3b15140d758a0da9bc6ab9a655958545d9c0c9f9c23e2bb265

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:55.192715Z digest=sha256:5af60185592ea6db13c3b1461bf0dee21ecd9c4a007174344e6816722c866384

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:55.584826Z digest=sha256:880576fde93f2360117fda68c73446234e370d0d737a227ba3aae879e18aaf9b

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:55.764816Z digest=sha256:bbbc042c0f2e2609b0ff337e349e5f92d06bc0e0d74948179402424406bf8f40

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:55.944962Z digest=sha256:90a74f7b62d03c44e3d0dde665f1a10f4f2410d5693e37aa67e9e3ec95174077

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:56.154743Z digest=sha256:cc7030df8837e694e45e6d3e146d9f6e19c76110bf782ddf26471624ae8a1e06

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:56.384824Z digest=sha256:c26ce76f096c3bd593e853cb410fab8951451b04d18e3ec9160fa98955e1175c

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.

source=pdf_text observed=2026-08-06T23:28:56.574864Z digest=sha256:9c4233f2bb170404cb3accc80e95118d5beaee5ae6fcc63b1be28f3fd1074507

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:56.924756Z digest=sha256:135022a9f6cee30e4cf26fa7535e0f5c0340faeed53705d8db8e6f5f9a271a2c

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:57.164675Z digest=sha256:70239f0f4319c4b01283bc9ca7675c6a39b19f7a83bcb4b88008e6cb3b7b4c29

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:57.324823Z digest=sha256:60e7d19ee256075c619a3df40c9944171fbecc5def9df9da58f1279819160b69

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:57.401196Z digest=sha256:782ae3cd390329ae6dc8b763a43f3c02eba3ef46ad27a92e7e6ff747ecd9f0a8

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:57.520264Z digest=sha256:11b3fc4d6254d964daffbd88abe51c2f712d5edf5bba390be03c19a2b219a497

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.

source=pdf_text observed=2026-08-06T23:28:57.664738Z digest=sha256:3c0feb0340a08de7b203a06b2008d621d16ce6096c73667994014dd23c5eee47

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:57.794746Z digest=sha256:5717cee5bb4ddb77b36b088d7f105e48796b3f9ce1fbe93f7aa21819d7eeb26b

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:58.054754Z digest=sha256:11497a8db1d0bda4e661111039e6bf4397057cbf7792804e44b64e8709dd411e

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:58.174943Z digest=sha256:b9144d617a67edbd5a4e860cc2ae602eb9562cceb4154d407904faf0daec3bd7

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:58.429855Z digest=sha256:a1be897f585f5daf272e5984cd9bc9704d3b750d1943a50d54319d3e6dd6e357

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:58.542936Z digest=sha256:6bd8949bad2a062922770e6dcad9a38aba0e9af2e449c6d0bbc9fa5bf7796b30

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:58.664351Z digest=sha256:1a7d5a4568a6a5722abe26f8528ffc6d43ddb58feaec535e7d610e6e75a27c5d

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:58.834820Z digest=sha256:fe6db0913b04af53e8fd25fca963c99e7cb270d80b2f9b6af20ac8cd0419dfea

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.

source=pdf_text observed=2026-08-06T23:28:58.999330Z digest=sha256:0b787355bf0ccd3b13dc1f3b6c96716e9beea94622a876db510ce426eb9bb476

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:59.137305Z digest=sha256:ce4a680848582ae5ab2161a3f10b74306202d42f2fe270801acb67ba1b559ff2

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:59.314750Z digest=sha256:c61cb39bfb1201268f95be044a07e06219533013e4e9114ad0e997d4852611d3

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.

source=pdf_text observed=2026-08-06T23:28:59.472454Z digest=sha256:ab00d51fc8bd0d55ec17542f82980f73c6c2ba4c322ff6245fed2edf4218c1dd

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.

source=pdf_text observed=2026-08-06T23:28:59.569249Z digest=sha256:e367f6be22580df95619f03130803e7999d088b8fe2c19382db03d28a153deae

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:59.774777Z digest=sha256:beaadabbaf1750c2fcc395a62927db3cfddebb80881d824652982cba07d612b3

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:59.954650Z digest=sha256:bf4942374c9d52f3ecb361e3b31013c097e28764b380ac20bd62b3811f8302b9

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:00.229514Z digest=sha256:dc835d42d4c8b2c90483a9114438b162661b037f5b23d90b23fa03a282a9efbe

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:00.424827Z digest=sha256:61d3f05794e42f0c24dfe11a2592e4fd25bc5b4a3d191523f63a2b16235759c3

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.

source=pdf_text observed=2026-08-06T23:29:00.678622Z digest=sha256:98e258d637bed6c76b8f4c4787a73b7cf19d6ad0ac4a84ffcad57e8ff87bcc7c

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:00.814751Z digest=sha256:1f526be42ec2ac4f6950ab2a7305da0c832a10da289646649ceecf0394faaa93

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:00.974627Z digest=sha256:c75d2b0fd89e96319263420c21cc9528e6cb81d5f4d1968f3d5e656e6907208b

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:01.135797Z digest=sha256:32ceaf123ce9a6858a9bd618aa9e32b25dd1b0bf2297b61b44809f1aa1431da7

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.

source=pdf_text observed=2026-08-06T23:29:01.254425Z digest=sha256:b931fd655e98182bc5bbda02842644e070cec1d51afd9e185f4b58179a9391c9

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:01.322007Z digest=sha256:a14f0e3cd7d0b16838818f6efe60513d4732fde736d6cfe9d5b29838cbb8d4a1

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:01.414308Z digest=sha256:d487442c7f3df89968275745f4bcea29e4d9a534d5090854062eadde96e73622

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:01.494806Z digest=sha256:561f8583301f137094bb40838f57a2fc3a2c17c7547095907c1295537a658a13

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:01.614757Z digest=sha256:9af47ae45b414876ecc3971edacba538c62012fc62748edc613f7fc310eef251

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:29:01.778892Z digest=sha256:e484294d71640b05d065e9cd4fe3dfd07ad164eb36208030a3c8813773a27ce4

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.

source=pdf_text observed=2026-08-06T23:29:02.024766Z digest=sha256:189022434b80a30f9e29baca502e0e88cfdea77ce7c4a8533747a127da37eb12

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.

source=pdf_text observed=2026-08-06T23:28:57.069195Z digest=sha256:3c117f4dd36714e0ca24272ade320bf9fadb06a82b451edad0ebdf29c00bc8a7

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:28:58.282382Z digest=sha256:14ab81f8ff35dc064c837e95baaacf2313d3225e186f5e75ebaeff6c2f34d71c

Pith citing papers

Observation e67127bd-4b1c-48bd-8502-e9d5cbf83d0c · inbound

Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption cites this paper.

Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption

Reference 41

Resolution
verified exact
local_arxiv, observed 2026-08-05T11:16:58.072958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T11:16:58.039873Z digest=sha256:f31074d9668b84b1eb15183b3db945b291a882c6bb250d84577813f76771556a