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Paper Citation Record · LEDGER

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption

As of 9 August 2026, this Paper Citation Record lists 91 of 91 outbound references and 0 inbound Pith citation observations for arXiv:2607.21895.

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

pith.paper-citation-record.v1
2607.21895 v1

Coverage vector

measured 91 of 91 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:31:13.569014Z

measured 91 of 91 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

91 of 91 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved91
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45b961e1-59c0-4381-8424-9eecf14fefa6 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 1

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no resolver link, observed 2026-08-01T06:31:05.897867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:05.897867Z digest=sha256:bebdd75c372ebde1d9951623fd0ef07fcf41a500b79b759b448749bd63dfe593

Observation cb9d8104-fd9f-45ac-b0f6-3db323ca4701 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:05.981418Z digest=sha256:cc166ee016f827a8861b017c2a5097c84cad6004ab267953a1ea46b6de49e4f6

Observation b25b0ac9-739e-43ba-a487-826df6b8b1c5 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 3

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no resolver link, observed 2026-08-01T06:31:06.099588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:06.099588Z digest=sha256:6e72d5ab7807f0c56c5b3aff38e06f59e90ce6cb59050c3c0eda36eab5336705

Observation e8a03ce7-fd93-4ab9-86e2-f3cc86f40d1a · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:06.204749Z digest=sha256:a431e4318376de792dcace42c78fe26877bcedfc30bd0a5ce8cb4e0b784eb319

Observation 94fea654-3536-497b-9d78-c2057e1159bf · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:06.294753Z digest=sha256:6e64c4febcfe803e3833229eae081a591ccec08c967c25a18b1928ef25551f2d

Observation ed7ecdf2-6cd0-480f-a12e-90c8f025be0f · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:06.384249Z digest=sha256:00ec724e89059e315789587ec799042e7fe97b9fab6ee846cef500c265946f4f

Observation 9905f79f-e5ff-45a8-b5a1-0a9a88fbcde6 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 7

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no resolver link, observed 2026-08-01T06:31:06.475162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:06.475162Z digest=sha256:9d648d7b8eff8ff359d6a2b6b54c638d2b90d0f94c620bc84822bf456ce2157f

Observation c905a949-06d2-4d0b-bde7-490144b0a051 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:06.591442Z digest=sha256:a923384c80407d11df42342abbea1ab03c63df47a5c402a2948a0f57770402b2

Observation e8f38673-f091-46cc-a29b-0ed5358edff7 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 9

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no resolver link, observed 2026-08-01T06:31:06.804765Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T06:31:06.804765Z digest=sha256:22d01da1cd965b10aab44b8bf3c9e6acfbcbda5370e03e7b7b8ffb942f86bb48

Observation 364ae701-6186-4477-a363-c328ad28aba7 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:06.894824Z digest=sha256:786c773fab30bc2129e3eb6b73cbaf1b9202bb0d125021bec4235e08d13454fc

Observation 0e57f1b0-08ea-40ed-a4fc-6b79f7267f85 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.025167Z digest=sha256:e493849fbdfe8b3a41c534403b8e3d7babeda9a7198abbf42aba99bd06ed128b

Observation 9a3b2e73-0e9d-4b34-bd1a-b792099badd9 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 12

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no resolver link, observed 2026-08-01T06:31:07.092275Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.092275Z digest=sha256:70e3b418220844340f9522dafcc0f729ccf02d8141fd65987a78ca4b89222b59

Observation aab9a1b9-97b1-44da-a56d-f5f6e3e52ec7 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 13

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no resolver link, observed 2026-08-01T06:31:07.136384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.136384Z digest=sha256:10db9c2560ebf2b175b12341e33157c722f3a6f85fc86303302937025b842161

Observation ddfc1140-4701-4285-934e-ada042c9043b · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.311195Z digest=sha256:db6de9bde5d62d41940605e2ae8c27a658a35e03a8c3779c385321b7d0232681

Observation 43e74259-bcce-4f0e-a61c-f0686a2ad695 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.381341Z digest=sha256:63209f06345dd867c857e3d1fee50676a5e4f00842138f37106a1cd14dd64726

Observation e8a12f4c-9aae-4c0c-a739-a7b560c56a9a · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 16

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no resolver link, observed 2026-08-01T06:31:07.531792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.531792Z digest=sha256:e63dcab268ee8e659c19e2b02e63f522e78bebbbf6e8699c37265390b9e78421

Observation aabcece2-52a2-44b9-a88e-a68304825690 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.586082Z digest=sha256:8084ec8f340ad8ca981bd6c166500fb33c73cd4e2677d94ebc338fed48c930bb

Observation 9b6f64fb-21b3-4b64-9831-d4a4a298dc96 · outbound

This paper cites InProceedings of the 6th Workshop on Encrypted Computing & Applied Homomorphic Cryptography.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption InProceedings of the 6th Workshop on Encrypted Computing & Applied Homomorphic Cryptography

Reference 18

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no resolver link, observed 2026-08-01T06:31:07.437271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.437271Z digest=sha256:f94eaee7040bb5d7747c59597ae96c427d8590072908b06747ee1bd2eb7dbd70

Observation 63c8e2c3-f4fa-4403-8d2d-56e37f6e8cc3 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 19

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no resolver link, observed 2026-08-01T06:31:07.745466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.745466Z digest=sha256:0bb25460a768d312a8075726f217888bdfd8a12584c1de1157a2d7c2c807d091

Observation 9233c6c6-f1ab-466c-b8fb-95473ae8482b · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.867552Z digest=sha256:efdc65de712498ab7a6f37008bac3a9f061f70153a3666bf72d240492d458a77

Observation 9b88f634-5b94-4181-a993-36bbca36a485 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:07.659755Z digest=sha256:b33d11fa0c103533b5b708123124aea16498ca660fdf3fc4775136ecdd346de2

Observation 1959572c-132c-4a25-bab2-55dd1b9f0b73 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-01T06:31:07.983290Z digest=sha256:f745b3f3343b33341323b75762129650ef3743fda70c50cadee9d9e78de8ab2c

Observation 602c0e50-a1a8-482b-b562-346c989799d5 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 23

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no resolver link, observed 2026-08-01T06:31:08.059802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.059802Z digest=sha256:3ebfb7cded8d57371a77436806817ca7044c6981798ec4e8ad474c6fa286aaeb

Observation 86d4f24c-aefd-484f-b4fb-8d0e2843c423 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 24

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no resolver link, observed 2026-08-01T06:31:08.111367Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.111367Z digest=sha256:b701ebeaf6ffd2966e4d41401d8d284354e5c2766ef9a661f4e64874542ddfaa

Observation fda91163-d2be-4253-9c6a-2bbbee1983ba · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 25

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source=pdf_text observed=2026-08-01T06:31:07.923042Z digest=sha256:9af5d30071f2c0e748fc86861e200c84e738d7b10f4cef25b5e95eedfe9e6a80

Observation 0e74933c-2772-4167-8bd8-bd71e509bcc0 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 26

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.272709Z digest=sha256:57e1392c19a361c9b7ed5be65882cb5a17e07416a304f6188e19b622f43ae837

Observation ace4c3ae-a4ba-4efd-b6d6-dfeaa1625984 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 27

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.353560Z digest=sha256:187810db43a35a0e413197b5b7c9a65537f4418030eda4718868337ed336007f

Observation b837c7ea-809a-4ed0-b7ec-d4b65500ba7d · outbound

This paper cites 2009.Foundations of cryptography: volume 2, basic applications.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption 2009.Foundations of cryptography: volume 2, basic applications

Reference 28

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.448614Z digest=sha256:82fd25f2760103bff912e19431e35161ac9b93705049a3a3ee089c84c0817f12

Observation e0b701b5-f2f2-4570-8f63-4a3e5a810774 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 29

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.218353Z digest=sha256:3f563bf0b3856c07759a0bde15e4b0e02223f2f5d8aca4e6d05ab0a907aa9274

Observation ade8abda-21f3-4174-abed-f7978c67f5ef · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 30

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.601674Z digest=sha256:3ce38382c1c9748d54e7de7996913e196872790544b70f5a2258a8402d96e873

Observation e4086d74-8101-4be9-84af-517be21a082d · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 31

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.674751Z digest=sha256:7e90be347f12cada9b9379d16a7d0b0291cf531e800df1b7e73c8611f547f93d

Observation 8af018ab-0a5b-4830-baf5-f94da8809c4b · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 32

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no resolver link, observed 2026-08-01T06:31:08.767602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.767602Z digest=sha256:41ae71ada8ba96dfbe280c017dd4ad5e87e618fcd337c7083069e760993fb1d8

Observation a8409bc2-9b35-4289-98cd-4d9c2c127d5e · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 33

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no resolver link, observed 2026-08-01T06:31:08.534748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.534748Z digest=sha256:1da4dd858e36c63cff36bd14190ebdeaa597a2f490e93a5df49e7881c8f88bf8

Observation 475d7c83-6e97-41d1-a5b5-ef9e8b5bb860 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.919220Z digest=sha256:bedec688fe95e8695bae42be3c9109a723b5f9ac23269032a39b15d74c510809

Observation 0202f4fc-a606-4750-9a99-9426289e667f · outbound

This paper cites Structured Pruning for Deep Convolutional Neural Networks: A survey.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Structured Pruning for Deep Convolutional Neural Networks: A survey

Reference 35

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no resolver link, observed 2026-08-01T06:31:08.970517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.970517Z digest=sha256:f1dbff15e9df7fcedf3230c6574062e5a768db5e92ba24d269fe7056968070d6

Observation 6cc6cb52-99a0-4f2b-9f87-210e0a91b58b · outbound

This paper cites CryptoDL: Deep Neural Networks over Encrypted Data.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption CryptoDL: Deep Neural Networks over Encrypted Data

Reference 36

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no resolver link, observed 2026-08-01T06:31:09.147766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:09.147766Z digest=sha256:8aa3408f380cabefad8c4f15556768f8bf58761d4fefadc51d106b74b7c54d6e

Observation 7cfad0e5-2342-4c00-b82d-ca068bac789f · outbound

This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 37

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:08.856358Z digest=sha256:4bf9e9e6e12c0efcb751b3b4182e96430807ef643125cc6704fa0de3b3af8746

Observation a0cb409d-f5c9-4e32-b4c0-faf3ce5e95d7 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 38

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:09.421716Z digest=sha256:43d41693447313a464c73a08aa61b1a50434d0881b9be36d95d66733153c1347

Observation 9fa5e0fa-0465-42f1-b6d8-47c62131b262 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 39

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:31:09.564219Z digest=sha256:a4bea9d093cf211fe2aebc393e9d059c0dc2f6cfad0bd242e24489ad70e8099b

Observation d48dc005-d5ea-4f2a-8a40-ba8c046599a8 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 40

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source=pdf_text observed=2026-08-01T06:31:09.683427Z digest=sha256:6de3814e32b02dbb2757f98fa8a9fbaefcf28568ed4f2d5ffc10536aa980d0b0

Observation 1a5f3978-9307-42b2-9250-20c5fc02c30c · outbound

This paper cites Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks

Reference 41

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source=pdf_text observed=2026-08-01T06:31:09.310951Z digest=sha256:94b5eb8eb2793176db96a6327beae5a43304179e72ffa5e89e117047dd855d7c

Observation e9c0686b-ad9c-4ea8-b3f8-ad381e9f589a · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 42

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source=pdf_text observed=2026-08-01T06:31:09.944303Z digest=sha256:3ce1ef0d834c932ebd3b945a6012d599209902da4326a38f43380baee59a5b0e

Observation 1094850d-421c-489f-b2aa-66d23c93c1be · outbound

This paper cites Efficient CNN Building Blocks for Encrypted Data.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Efficient CNN Building Blocks for Encrypted Data

Reference 43

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source=pdf_text observed=2026-08-01T06:31:10.013543Z digest=sha256:0be3a5662cbb28ec895c29642a8b6997f1fe36580f4ed70cfdc3f4a1d40f15af

Observation 4ec754f3-b517-4317-adb3-fde1e532236d · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 44

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source=pdf_text observed=2026-08-01T06:31:10.078163Z digest=sha256:9075e2afacadaeb05bbe988f2c289c6180bb97e257367427d26c1210c9d1c5ce

Observation d81243b6-a5c2-4ab6-980c-d99cc1aeaee0 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 45

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source=pdf_text observed=2026-08-01T06:31:09.834549Z digest=sha256:aed723815f88f884f3657da9649a03ab15742e16651384018f7c5fb0503dc636

Observation 3434b949-db32-4c96-bdfb-8be3213e5747 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 46

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source=pdf_text observed=2026-08-01T06:31:10.209739Z digest=sha256:02c8f585c74e1fe602fbf2ef957ab167d082e42d3a24a95245941b58fb1049ae

Observation f75749cb-993d-41f9-aadd-0b9279abd6a0 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 47

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source=pdf_text observed=2026-08-01T06:31:10.298361Z digest=sha256:9274181469fcd4a9cc93ad181512acb01e2643db1d6ccbcdd7d645cf0a7caa10

Observation 8ef0b515-a0d1-42d6-a74e-e72102319474 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 48

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source=pdf_text observed=2026-08-01T06:31:10.363029Z digest=sha256:1b01c1aded6a5b69cf69e72646e4250411d4cdeee3f9fb8945993629d6b30203

Observation 0d22ffc6-0eb1-4b2c-8923-005af505fa41 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 49

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source=pdf_text observed=2026-08-01T06:31:10.148356Z digest=sha256:9e7026aea2ab875fae55e4f9b2c4d95ce48b30fd3e729c244c3385677d9c0898

Observation 0e16c952-cb48-494f-bf9d-f029697f58b9 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 50

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source=pdf_text observed=2026-08-01T06:31:10.526696Z digest=sha256:43783f4be6a77e9be0c64428670df3203994f3b0033513b20ff316026a92d362

Observation 5d3587dc-d6d2-4636-9676-af8167d39235 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 51

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source=pdf_text observed=2026-08-01T06:31:10.633375Z digest=sha256:00abe407f0ab59745217455020468fb818ae92da831ea1b7bca962645565a83d

Observation f9a8276c-b79c-4d63-a18c-38338b27341e · outbound

This paper cites Pruning Filters for Efficient ConvNets.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Pruning Filters for Efficient ConvNets

Reference 52

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source=pdf_text observed=2026-08-01T06:31:10.696232Z digest=sha256:e8b8fbcc40700c5c5c0499323783fdad67c9fb2ba885ad08a14fea12b033c415

Observation cdd939b3-5e6d-4129-a3d3-dfa849305256 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 53

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source=pdf_text observed=2026-08-01T06:31:10.455672Z digest=sha256:608988369e02331147ea5d12493f7520dd0214deab8216c77710b28bde64a050

Observation bcd82583-5606-4ae3-8331-3cf72f8e2a68 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 54

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source=pdf_text observed=2026-08-01T06:31:10.837693Z digest=sha256:12b8f326e016eb85d77ef5a729e7dd80660fec1cbdcb7510bcf6be59ff17ee7c

Observation 0914e09f-f9ee-49e7-8923-e2b452e50334 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 55

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source=pdf_text observed=2026-08-01T06:31:10.907028Z digest=sha256:2db0c651efda5f867c40612675f4edec6af2d020a03cc9164cf5998981829c74

Observation 600374c0-9106-48b6-b14a-616676b7ca1e · outbound

This paper cites Rethinking the Value of Network Pruning.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Rethinking the Value of Network Pruning

Reference 56

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source=pdf_text observed=2026-08-01T06:31:10.954853Z digest=sha256:f1f8fbfa68f1bfa72871093bb9719eaf65e29ae4621e29353fca326dd8eee6c2

Observation c2f31bfa-2d85-48fd-bc03-8aa087139a84 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 57

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source=pdf_text observed=2026-08-01T06:31:10.768066Z digest=sha256:22e9a44683594f0cfbbc99065746945aa948078294c317d484947e3e780a4702

Observation 041de178-bd13-4bf2-9984-e800aa764f01 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 58

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source=pdf_text observed=2026-08-01T06:31:11.211959Z digest=sha256:44d033a2e27ee94bd9ddc080cc4fe5176d4d083641008590626ace9f8c875ace

Observation 4b7b0662-a37d-4dd7-8872-e52d7f3781b3 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 59

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source=pdf_text observed=2026-08-01T06:31:11.252970Z digest=sha256:d7d898747451cdfc26bc3bd299d005eca31895715e5c2688c73bd8528e6c4928

Observation 390d699b-728a-4566-8d97-69dbf6b564a0 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 60

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source=pdf_text observed=2026-08-01T06:31:11.340553Z digest=sha256:c625a40fab83165e18de0be2bc60cfc076e01c4aa32027038f83b236a67b1c68

Observation 0f00e17c-3ac2-4e3b-8b28-a1801a741349 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 61

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source=pdf_text observed=2026-08-01T06:31:11.097782Z digest=sha256:72d6465d30a228b9e500874877d684222a4a1f7c3eab66cdab72bbc3b3184b3c

Observation 1c89b1f0-e22e-4a55-bced-40422d93f60c · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 62

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source=pdf_text observed=2026-08-01T06:31:11.581783Z digest=sha256:82f4c42d04dd41898e6a74c939e5d0d8a4ce45eda043ad2a81bc4e50e1cf29dd

Observation b0ec78e4-732a-4e9d-964c-ce37ed906a97 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 63

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source=pdf_text observed=2026-08-01T06:31:11.644669Z digest=sha256:61399302817a9c37417003ee3867cfef4e3e1fd529efd8058507c561c4b57da2

Observation f7de8a44-3dc7-439a-a00d-55b6b93b8e3c · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 64

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source=pdf_text observed=2026-08-01T06:31:11.715137Z digest=sha256:d5f2fb2826ce74d2734f0a02d778d663f94a672ec6bb77b93310ed98c3e0df80

Observation c3647e0f-0b28-4cc4-83b2-8f974ee14b3d · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 65

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source=pdf_text observed=2026-08-01T06:31:11.779767Z digest=sha256:bd2f0ab06daec89e38692bfa9c54d2326d58e269a975c4985a2f141cf79e0e27

Observation 156affc7-5bd4-48bd-9760-81a624fcbd5e · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 66

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source=pdf_text observed=2026-08-01T06:31:11.494047Z digest=sha256:4d077f5a983105df20b459c20af6fc574c3d84ff1e523fe3d9d40252e175694b

Observation 3989ca4c-5b9c-4530-a7bf-6f203cb13ca5 · outbound

This paper cites Searching for Activation Functions.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Searching for Activation Functions

Reference 67

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source=pdf_text observed=2026-08-01T06:31:11.897478Z digest=sha256:f70c32a4369df97e66cd72fd3c11f3d720a94df54190cb08ddc85d353c43f625

Observation c176d36b-0ee9-497c-858d-7830e324370e · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 68

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source=pdf_text observed=2026-08-01T06:31:11.949174Z digest=sha256:3312fdf3cf2384e0eee06495a8b0da53660558eaeb14b276b5a5cfc5640f4e98

Observation bf733f98-8f0b-44c6-b2bc-7c6c56df3c9b · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 69

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source=pdf_text observed=2026-08-01T06:31:12.014721Z digest=sha256:f28182871dee8be91e4541a384da019395d6432df182c28336b45ca112e36c35

Observation 94534a1e-451e-4b6a-ae20-37207b084b13 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 70

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source=pdf_text observed=2026-08-01T06:31:12.074087Z digest=sha256:7dd9a310fdad62f5d67addd025decf35361d024b7fde9401aee08fbbf10415e4

Observation 74d44454-c287-4753-ac67-5d28eb1bb92d · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 71

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source=pdf_text observed=2026-08-01T06:31:11.836193Z digest=sha256:5a7dcab367a3ac133fcdd839e3e0110b3fca1752518e0c142420df585cdc4f4e

Observation 85c17a8f-daa8-4796-adf1-7478efdf0a56 · outbound

This paper cites Microsoft SEAL (release 4.0).

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Microsoft SEAL (release 4.0)

Reference 72

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source=pdf_text observed=2026-08-01T06:31:12.304919Z digest=sha256:cef72f2906886f2403707edde7fa59208e2c1fd437cfb3bd93d232013f871b6f

Observation 5cfbfcfc-55d5-44d6-b606-727c45c85cfa · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 73

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source=pdf_text observed=2026-08-01T06:31:12.413502Z digest=sha256:8ac82ea87bcf3229ab7a069abd6d34431e3cb1be84b706e58cf87cf89763fa56

Observation 1290af96-1e9e-4160-99b4-b74784524c6a · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 74

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source=pdf_text observed=2026-08-01T06:31:12.529704Z digest=sha256:3b57fa04338951d0082c376bbbcea1742e0b615740dc6557c6147896b3c9bb39

Observation d782cb6d-4c7f-4b5a-a7c7-3710f1c07f0f · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 75

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source=pdf_text observed=2026-08-01T06:31:12.630148Z digest=sha256:945b9e4692787a092f95e34807a401210986c1611a8b97bc868366d8b9c95d21

Observation 61db2a21-3205-4a04-b946-e066fdda58fa · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 76

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source=pdf_text observed=2026-08-01T06:31:12.167915Z digest=sha256:ad3d31ea20d70391f460dadd2cb6290839dfb1abc030a2604d850466df678df7

Observation f2f09925-8e9a-4151-a561-7a2397fc4774 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 77

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source=pdf_text observed=2026-08-01T06:31:12.824689Z digest=sha256:335890bf626578f75c1760e1ebf0c85770e28a465f84ce60a7a822cfa12a816d

Observation a0dd9c1e-f491-4eae-8113-8cd955b73414 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 78

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source=pdf_text observed=2026-08-01T06:31:12.940415Z digest=sha256:3bbe5a5be54176e61028c05183ff0c1a4d9d32864b890421e3f8d73ad7b84a39

Observation e7768717-bc3b-41b7-83e6-a395b8c9193c · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 79

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no resolver link, observed 2026-08-01T06:31:13.032157Z

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source=pdf_text observed=2026-08-01T06:31:13.032157Z digest=sha256:468bffeabafe255225844c907af2ec25cdc8709d957923447283bb98e18e30d5

Observation df9a4438-73ed-42bd-bf13-73221b2920c6 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 80

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source=pdf_text observed=2026-08-01T06:31:13.121723Z digest=sha256:f57795f35def0ecaa321044eefa59ba23fe041be918f69e3e042c87ba10f66f6

Observation 14744be4-40de-4dda-a488-84fa00d65473 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 81

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source=pdf_text observed=2026-08-01T06:31:12.717326Z digest=sha256:ae5f5f89d92936329a6018fca079466d5066727525c0f041000cbf6047dd7700

Observation 46acf29a-2708-426c-a7fc-049796a6b078 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 82

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source=pdf_text observed=2026-08-01T06:31:13.458873Z digest=sha256:fac029d58a88f7c4a79eef5fc93c42498b62ba8d97a7e7eb6c080f0b20bbfe11

Observation ef5cf795-6169-4dfc-9151-7d21cfee9c2d · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 83

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source=pdf_text observed=2026-08-01T06:31:13.523900Z digest=sha256:a35ed0bc020370744c2a99c229c795a720e164a8b81f43ad12405b2f916b4a5c

Observation bd1fd820-845b-4a9c-90ea-38d08b94b5cb · outbound

This paper cites InInternational Conference on Machine Learning.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption InInternational Conference on Machine Learning

Reference 86

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source=pdf_text observed=2026-08-01T06:31:13.227572Z digest=sha256:bbfb4e1628c03838e0cf48b498b2372f32f20ef386619c139cada5d0a1fe497a

Observation 5419ef46-cab4-47c4-8bc1-aad4a0e89019 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 87

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source=pdf_text observed=2026-08-01T06:31:13.322489Z digest=sha256:d3ab6b12bab240f7a7b6089f2e329dd7ad8a4323de33c49beac9d02e0a3fc766

Observation baba57f0-1bfa-4b2c-b9b2-b06fd9e7df20 · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 88

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source=pdf_text observed=2026-08-01T06:31:13.389032Z digest=sha256:5fbe96e8a3c109a1586bd432418867bb85199054327ceb2fed8328d582a1078e

Observation f620c2b0-0c57-42dd-bd12-673f9a4825bf · outbound

This paper cites • CIFAR10.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption • CIFAR10

Reference 91

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source=pdf_text observed=2026-08-01T06:31:13.569014Z digest=sha256:f1a88a557939f524d3ab6f5d07b50b1e6b202d798dcc02713d71601e51cdbd9d

Observation d7eab8bd-c098-4eaf-9a19-19b1f1b923ea · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 2014

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source=pdf_text observed=2026-08-01T06:31:07.805548Z digest=sha256:538cef3f28616e2ee6f87550077855cdda48ea8a7977d76bf3c9916f12d0bb3d

Observation a68a4db1-f59a-4960-a14d-a5bddc747f70 · outbound

This paper cites Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference

Reference 2018

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source=pdf_text observed=2026-08-01T06:31:07.234828Z digest=sha256:841bd8cfe573552c2dd057762d3e884186dc2132c52a4ffbd2082d6cde85d151

Observation e21a852e-8900-4b58-8baa-80bb51b8f0ea · outbound

This paper cites an unresolved cited work.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption Unresolved cited work

Reference 2020

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no resolver link, observed 2026-08-01T06:31:11.431853Z

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source=pdf_text observed=2026-08-01T06:31:11.431853Z digest=sha256:5277743fa5f81c6f833a1c3892dcf98b556cbf9e8081399432bc42c32f1815ba

Observation a9a3767a-b379-49d8-84c8-bdbe5de2b597 · outbound

This paper cites InAAAI, Vol.

PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption InAAAI, Vol

Reference 2022

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source=pdf_text observed=2026-08-01T06:31:06.684775Z digest=sha256:2676c89a340a8bdd956ba762de8b81a5ab6982e29f8b1ab92e3eddd4146c3823

Pith citing papers

No inbound Pith citation observations are available.