TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models | Paper Citation Record

Observation 643f8c6e-fd56-42fc-9b24-07c09f37e176 · outbound

This paper cites Knockoff Nets Demo Code.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Knockoff Nets Demo Code

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This paper cites ML-Doctor Demo Code.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models ML-Doctor Demo Code

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models One-time pad

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Android 7.0 Compatibility Definition

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Artifact

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Full Supplementary

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models OP-TEE documentation Raspberry Pi 3

Reference 7

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Artifact for LLM

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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This paper cites Ng, and Honglak Lee.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Ng, and Honglak Lee

Reference 26

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Lightweight Convolutional Representations for On-Device Natural Language Processing

Reference 29

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 31

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Lauter, Michael Naehrig, and John Wernsing

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Confidential Inference via Ternary Model Partitioning

Reference 42

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 47

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Yu, and Xuyun Zhang

Reference 48

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Reference 49

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022

Reference 50

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Reference 52

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware

Reference 55

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Edward Suh

Reference 56

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Chandrakasan

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TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 62

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Source-reported events for the cited work

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Observation ea665e22-b3b2-4802-a553-01af658663e3 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.943191Z

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Observation 728f9154-3e8f-4b9c-9a1c-fe6a2ed3100d · outbound

This paper cites On the Effectiveness of Regularization Against Membership Inference Attacks.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models On the Effectiveness of Regularization Against Membership Inference Attacks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.947478Z

Source-reported events for the cited work

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Observation 9bc0a84d-7037-4e48-8eae-9906996a4eb5 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.939172Z

Source-reported events for the cited work

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Observation 337f2da7-cdd0-4881-82e1-cba1155f5f37 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.956557Z

Source-reported events for the cited work

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Observation 2128416f-181f-4d11-8b80-1a287671cb7e · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.961644Z

Source-reported events for the cited work

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Observation 858abe51-cf85-4c30-b2fb-1446143f1788 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.952467Z

Source-reported events for the cited work

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Observation 03c31983-5997-42ab-885c-d7c2516b4183 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.970877Z

Source-reported events for the cited work

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Observation ab810b80-d82c-4e02-b0ea-946477018765 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 70

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Source-reported events for the cited work

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Observation fd256c4e-ce67-4e00-8965-0b139e3bbba8 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.966108Z

Source-reported events for the cited work

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Observation f7555294-4fef-4dff-a571-ee8e1631f93e · outbound

This paper cites Molloy, and Dong Su.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Molloy, and Dong Su

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.984053Z

Source-reported events for the cited work

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Observation 5b4833bf-c71d-485f-9aa5-ab355128e852 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.987806Z

Source-reported events for the cited work

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Observation aa22915e-ba1e-4111-b736-e013ff2efc0c · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.979692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31006564-d1b1-4d7c-8f70-f0ba738b99f3 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.995559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c0cbd72-f705-4463-ac3c-2f8578314ed2 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.999820Z

Source-reported events for the cited work

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Observation f10ceab6-d963-4f71-b911-584ba916fe0d · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:24.991458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a6f5ff7-ef35-4352-88a4-3531ab111517 · outbound

This paper cites Zomaya, and Minyi Guo.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Zomaya, and Minyi Guo

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.008008Z

Source-reported events for the cited work

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Observation 2b552ec7-1d09-45a0-b2bd-341621e2e7eb · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.011605Z

Source-reported events for the cited work

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Observation b6b3514c-e7f8-439e-8e7e-dca534b20f04 · outbound

This paper cites TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.003751Z

Source-reported events for the cited work

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Observation 57edd652-09a6-4ed6-99ff-03e73311b3f0 · outbound

This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.018966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab6708d9-54ab-4a9e-9a84-4080ef2e651b · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.022775Z

Source-reported events for the cited work

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Observation 29371ebe-ce0b-4807-a8e7-ad245a8b1e3b · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.015162Z

Source-reported events for the cited work

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Observation d4e164a7-4b63-4cda-9ec0-9f3e2d15b0cd · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.034091Z

Source-reported events for the cited work

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Observation f2ade33a-c858-4c95-926e-20eb7f83617a · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.038000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a3b716e-89dd-455d-bba2-96c798b2fedc · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.026561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afb90b72-b337-4cf9-8c70-292b5a3706fe · outbound

This paper cites In 2021 IEEE Symposium on Security and Privacy (SP).

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models In 2021 IEEE Symposium on Security and Privacy (SP)

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.030496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b58f324-080c-4e82-9148-89c7b4bcc25c · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.049578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fdffa5c-b24f-4022-97af-528065c28832 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.053164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef0945c2-b8e9-469f-ad05-0ce86dc0c5fd · outbound

This paper cites MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-08-12T20:14:25.431814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8af40926-cfff-40ad-b1c4-08ca27e3e583 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.045814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c26bfcfb-b284-4a75-91cb-5c4eb267c5db · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.065247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c640dfac-4190-4320-87d0-602ac47b0632 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.069971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a6e27a4-d7b2-45de-901a-624d3959a7c2 · outbound

This paper cites Rozas, Hisham Shafi, Vedvyas Shanbhogue, and Uday R.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Rozas, Hisham Shafi, Vedvyas Shanbhogue, and Uday R

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.057208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b155e48a-63ff-4954-ae5a-e4974c99e62e · outbound

This paper cites Dibbo, Ehsanul Kabir, Ninghui Li, and Elisa Bertino.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Dibbo, Ehsanul Kabir, Ninghui Li, and Elisa Bertino

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.060949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 000e036e-459d-40ed-a963-889d5d9ff120 · outbound

This paper cites Tullsen, and Hadi Esmaeilzadeh.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Tullsen, and Hadi Esmaeilzadeh

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.083162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 399c3ca5-2309-4477-8f14-352f61f594cb · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.086947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation babd23f8-c1b0-46d0-8ca7-c6e7d2fe13cf · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.074308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df093e89-a5fa-401b-abc7-51b3652abe27 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.078726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c5b115d-b633-4a9d-b453-c01c02f9bd15 · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.097576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8516e641-2637-4fc0-ad7f-8031570f320c · outbound

This paper cites Oswald, Flavio D.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Oswald, Flavio D

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.101829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be3869f2-f44b-4b81-a73c-900b5e59737a · outbound

This paper cites an unresolved cited work.

TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models Unresolved cited work

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-12T20:14:25.090345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers