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Paper Citation Record · LEDGER

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.18342.

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

pith.paper-citation-record.v1
2607.18342 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:39:40.298059Z

measured 35 of 35 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation 62201125-e745-44a8-b1b0-362d113fb4c5 · outbound

This paper cites Homomorphic encryption for arithmetic of approximate numbers,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Homomorphic encryption for arithmetic of approximate numbers,

Reference 1

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source=pdf_text observed=2026-08-01T17:39:36.207814Z digest=sha256:4a16bce98b10ccb81940334bd12a88ceb174b4e96a41b934901675db1092a59f

Observation fa644afc-1ea3-4038-801f-b0dc627129c1 · outbound

This paper cites Algorithms in HElib,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Algorithms in HElib,

Reference 2

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Observation 82e7f689-5ea0-421e-bdba-6bc4e454cfd2 · outbound

This paper cites Hunter: HE-friendly structured pruning for efficient privacy-preserving deep learning,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Hunter: HE-friendly structured pruning for efficient privacy-preserving deep learning,

Reference 3

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Observation 4088eaf0-7370-4242-af70-007cb3b7c773 · outbound

This paper cites SpENCNN: Orchestrating encoding and sparsity for fast homomorphically encrypted neural network inference,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption SpENCNN: Orchestrating encoding and sparsity for fast homomorphically encrypted neural network inference,

Reference 4

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source=pdf_text observed=2026-08-01T17:39:36.539580Z digest=sha256:93e1759c84f9612592d7c05d9fb3f4b252b46808d08a23f29152ef0f6d6761b0

Observation 4fc63899-05ba-4444-abba-e92d50af23c1 · outbound

This paper cites MOSAIC: A prune- and-assemble approach for efficient model pruning in privacy-preserving deep learning,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption MOSAIC: A prune- and-assemble approach for efficient model pruning in privacy-preserving deep learning,

Reference 5

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source=pdf_text observed=2026-08-01T17:39:36.656687Z digest=sha256:d5a23334a96e05919a1d6d6b57bf588bd079f0a2bc226096691dd63a658b5bf4

Observation 1f65b7f3-782f-465d-9ffb-6d4f4feec685 · outbound

This paper cites PrivCirNet: Efficient private inference via block circulant transformation,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption PrivCirNet: Efficient private inference via block circulant transformation,

Reference 6

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Observation af8eac3f-1a0c-4964-a8cc-73b5d6a156cd · outbound

This paper cites The soft error problem: An architectural perspective,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption The soft error problem: An architectural perspective,

Reference 7

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Observation d67af2f8-e725-4033-9f33-96d7ff4c86fc · outbound

This paper cites Radiation-induced soft errors in advanced semiconduc- tor technologies,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Radiation-induced soft errors in advanced semiconduc- tor technologies,

Reference 8

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source=pdf_text observed=2026-08-01T17:39:37.039560Z digest=sha256:9a6a2fdb031201c4b9c71059e44f682c5dde6ec6c435cb115cc6ff2043930d9b

Observation 44c0c5dc-f34f-41bb-8ea4-7f8593a08509 · outbound

This paper cites Ares: A framework for quantifying the resilience of deep neural networks,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Ares: A framework for quantifying the resilience of deep neural networks,

Reference 9

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Observation b744bac5-5b1f-4c25-8f35-f0f932c7a5bc · outbound

This paper cites Understanding error propagation in deep learning neural network (DNN) accelerators and applications,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Understanding error propagation in deep learning neural network (DNN) accelerators and applications,

Reference 10

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Observation c586ae8c-929a-4d76-9760-189887ed59c1 · outbound

This paper cites Optimizing selective protection for CNN resilience,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Optimizing selective protection for CNN resilience,

Reference 11

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source=pdf_text observed=2026-08-01T17:39:37.453908Z digest=sha256:c85654523d49f6e1367ff83a0ab63bdd08d893579a06c7f6278de5883ae16844

Observation b6f76b57-99f7-4a84-9255-4fced215fd08 · outbound

This paper cites BinFI: An efficient fault injector for safety-critical machine learning systems,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption BinFI: An efficient fault injector for safety-critical machine learning systems,

Reference 12

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source=pdf_text observed=2026-08-01T17:39:37.599892Z digest=sha256:26022f01e0b99515cbd3d35f8812b3092ef49d5447112fa2ae364a4129a6efe0

Observation 211074f1-153a-42c9-98e1-5f1618c3b9ff · outbound

This paper cites Reliability analysis of fully homo- morphic encryption systems under memory faults,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Reliability analysis of fully homo- morphic encryption systems under memory faults,

Reference 13

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source=pdf_text observed=2026-08-01T17:39:37.715293Z digest=sha256:bb4f94633d2473be08cd6e456df8c429d8ebc7dadcf8384ae9af0ef203d1842a

Observation 0296922e-3fd8-465d-b5fd-147524e3820b · outbound

This paper cites On the vulnerability of FHE computation to silent data corruption,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption On the vulnerability of FHE computation to silent data corruption,

Reference 14

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Observation 4e2df855-43cb-496d-a927-92776ef2ad86 · outbound

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

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy,

Reference 15

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source=pdf_text observed=2026-08-01T17:39:37.919894Z digest=sha256:150c374266eb248d7d89fd7f85635d3bfb180cbddadecce4a05a0c2a5b739fa6

Observation 0d27a4a5-be91-475b-988b-3f7d406202cb · outbound

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

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption GAZELLE: A low latency framework for secure neural network inference,

Reference 16

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Observation 8498c6d5-a3a5-456d-94b5-5906abbc53dc · outbound

This paper cites CryptoNAS: Private inference on a ReLU budget,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption CryptoNAS: Private inference on a ReLU budget,

Reference 17

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Observation 47bd67a4-bd03-494b-ab4e-9ed5d2963270 · outbound

This paper cites Privacy-preserving machine learning with fully homomorphic encryption for deep neural networks,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Privacy-preserving machine learning with fully homomorphic encryption for deep neural networks,

Reference 18

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Observation 385a92fa-abc1-4302-838a-cd43800e74b8 · outbound

This paper cites HyPHEN: A hybrid packing method and its optimizations for homomorphic encryption-based neural networks,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption HyPHEN: A hybrid packing method and its optimizations for homomorphic encryption-based neural networks,

Reference 19

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Observation a2d48553-426d-4ad6-886f-fbe5ff5c0a27 · outbound

This paper cites Efficient pruning for machine learning under homomorphic encryption,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Efficient pruning for machine learning under homomorphic encryption,

Reference 20

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Observation cee88647-2f80-44e6-a23d-bea04505995f · outbound

This paper cites MOFHEI: Model optimizing framework for fast and efficient homomor- phically encrypted neural network inference,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption MOFHEI: Model optimizing framework for fast and efficient homomor- phically encrypted neural network inference,

Reference 21

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source=pdf_text observed=2026-08-01T17:39:38.710998Z digest=sha256:93d0eda7f5ec0f008677b9a2ea2b79e54f7ccba4280c89767e7a8dfb87d63e2b

Observation 2977fe1c-79ed-49b8-b824-5c53a4959fa6 · outbound

This paper cites AutoFHE: Automated adaption of CNNs for efficient evaluation over FHE,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption AutoFHE: Automated adaption of CNNs for efficient evaluation over FHE,

Reference 22

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Observation 31d4e0c1-5728-47eb-9f1a-8bad3857e2b6 · outbound

This paper cites Pruning filters for efficient convnets,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Pruning filters for efficient convnets,

Reference 23

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Observation caab124c-fca5-4b94-954b-8c1491943c71 · outbound

This paper cites Channel pruning for accelerating very deep neural networks,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Channel pruning for accelerating very deep neural networks,

Reference 24

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source=pdf_text observed=2026-08-01T17:39:39.047951Z digest=sha256:e5e502cae0ea495e82e6160e39a4db06f99e0910cc4e084cbdcca37009c2b944

Observation 497d4357-83ef-44a9-af18-a82b6c2939c0 · outbound

This paper cites TFHE: Fast fully homomorphic encryption over the torus,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption TFHE: Fast fully homomorphic encryption over the torus,

Reference 25

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source=pdf_text observed=2026-08-01T17:39:39.160804Z digest=sha256:ce236e82098872679e9a00b9a6597af798263dbcd3bb2041c9c6750c43c9716e

Observation 33a7b735-7022-49b0-95dd-b71dba69f2e3 · outbound

This paper cites GlitchFHE: Attacking fully homomorphic encryption using fault injection,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption GlitchFHE: Attacking fully homomorphic encryption using fault injection,

Reference 26

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Observation 35a56f5d-d421-4451-97e8-3f9710edc99e · outbound

This paper cites Self-learning activation functions to increase accuracy of privacy-preserving convolutional neural networks with homomorphic encryption,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Self-learning activation functions to increase accuracy of privacy-preserving convolutional neural networks with homomorphic encryption,

Reference 27

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source=pdf_text observed=2026-08-01T17:39:39.373493Z digest=sha256:ebec8ed24fdd87fbf5ed247375c2d48c26e775be72e9a241dfd8a59f24703e10

Observation b8f83672-35cc-45a5-b9d9-c494011e6300 · outbound

This paper cites Highly accurate cnn inference using approximate activation functions over homomorphic encryption,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Highly accurate cnn inference using approximate activation functions over homomorphic encryption,

Reference 28

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Observation 788dc79f-f058-4d9e-ae4c-179f6147d127 · outbound

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

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Efficient bootstrapping for approximate homomorphic encryption with non-sparse keys,

Reference 29

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Observation e6ce595d-5796-408c-a52e-9caa001f5cd1 · outbound

This paper cites Deep residual learning for image recognition,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Deep residual learning for image recognition,

Reference 30

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Observation 290c37a0-fe3f-402e-a6f5-a80177f0a0a9 · outbound

This paper cites Learning multiple layers of features from tiny images,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Learning multiple layers of features from tiny images,

Reference 31

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source=pdf_text observed=2026-08-01T17:39:39.820093Z digest=sha256:5ccc4ff06d3200481741d9b1c18233a35cdf8f89e63b6ff89dbcffee0acacb28

Observation b9072b68-fa52-4fcf-abde-654d5d7db0bd · outbound

This paper cites DeepReDuce: ReLU reduction for fast private inference,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption DeepReDuce: ReLU reduction for fast private inference,

Reference 32

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source=pdf_text observed=2026-08-01T17:39:39.963171Z digest=sha256:229b6c1be25b56158a145677fa70b2f0853a8a575b56cfa63f9b19daed6ffd09

Observation 9ac69b9c-29ac-4288-810a-9f24384023bc · outbound

This paper cites OpenFHE: Open-source fully homomorphic encryption library,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption OpenFHE: Open-source fully homomorphic encryption library,

Reference 33

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source=pdf_text observed=2026-08-01T17:39:40.062981Z digest=sha256:8e981784e4f5be7890642e029a0fe819a4e37687c7bcc2b2fa965eced5a74933

Observation 05742c53-1731-43f5-8876-0d103aefed89 · outbound

This paper cites Statistical fault injection: Quantified error and confidence,.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption Statistical fault injection: Quantified error and confidence,

Reference 34

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source=pdf_text observed=2026-08-01T17:39:40.194664Z digest=sha256:1b9a9e93db9f79157735b55bc080173236e1d2482ec9379e556861f7e121f09d

Observation f741a7cc-450d-4305-93aa-e5d47dd86d25 · outbound

This paper cites AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference.

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference

Reference 35

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Pith citing papers

No inbound Pith citation observations are available.