Practical Anonymous Two-Party Gradient Boosting Decision Tree | Paper Citation Record

Observation 0fefe17b-3402-4dc2-a803-bcf057f2935c · outbound

This paper cites TitAnt: Online real-time transaction fraud detection in Ant Financial,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree TitAnt: Online real-time transaction fraud detection in Ant Financial,

Reference 1

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Observation 8f40272b-76d5-429f-a3db-f2d1f5302266 · outbound

This paper cites Where medical statistics meets artificial intelligence,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Where medical statistics meets artificial intelligence,

Reference 2

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Observation 66b8be88-65e8-480f-9d43-e2435f19e67e · outbound

This paper cites Model ensem- ble for click prediction in Bing search ads,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Model ensem- ble for click prediction in Bing search ads,

Reference 3

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Observation daa7e9d3-2c4a-4025-a377-57c20a0c4213 · outbound

This paper cites SoK: Cryptographic neural-network computation,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree SoK: Cryptographic neural-network computation,

Reference 4

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Observation 80ec16fa-3e3c-49b2-9b7d-e6d72f533d2e · outbound

This paper cites Squirrel: A scalable secure two-party computation framework for training gradient boosting decision tree,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Squirrel: A scalable secure two-party computation framework for training gradient boosting decision tree,

Reference 5

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Observation 5556757e-3517-4fd8-b05a-a1eb4a276685 · outbound

This paper cites PriVDT: An efficient two-party cryptographic framework for vertical decision trees,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree PriVDT: An efficient two-party cryptographic framework for vertical decision trees,

Reference 6

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Observation 176655d0-c465-4f29-92ad-fb487d955c47 · outbound

This paper cites Large-scale secure XGB for vertical federated learn- ing,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Large-scale secure XGB for vertical federated learn- ing,

Reference 7

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Observation 6f43dde8-f1fd-4a7c-ab14-ff9005df2dcd · outbound

This paper cites Privacy preserv- ing vertical federated learning for tree-based models,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Privacy preserv- ing vertical federated learning for tree-based models,

Reference 8

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Observation 19a0b60e-93ea-44af-9543-f5d925a25e86 · outbound

This paper cites Efficient private matching and set intersection,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Efficient private matching and set intersection,

Reference 9

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Observation 7f110866-3a1b-4c6d-8a42-1d5d5f1476d5 · outbound

This paper cites Efficient batched oblivious PRF with applications to private set intersection,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Efficient batched oblivious PRF with applications to private set intersection,

Reference 10

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Observation a47f9dc0-d457-4788-b6ba-941d80024554 · outbound

This paper cites Unique in the shopping mall: On the reidentifiability of credit card metadata,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Unique in the shopping mall: On the reidentifiability of credit card metadata,

Reference 11

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Observation 22526bf7-bb8c-4547-95bf-876c9129556f · outbound

This paper cites On deploying secure computing: Private intersection-sum-with-cardinality,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree On deploying secure computing: Private intersection-sum-with-cardinality,

Reference 12

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Observation 1a669a5b-bdb0-4368-88ea-6e52798b0203 · outbound

This paper cites Efficient circuit-based PSI with linear communication,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Efficient circuit-based PSI with linear communication,

Reference 13

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Observation 76fa911b-e2be-495e-9545-4163cd640462 · outbound

This paper cites VOLE-PSI: Fast OPRF and circuit- PSI from vector-OLE,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree VOLE-PSI: Fast OPRF and circuit- PSI from vector-OLE,

Reference 14

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Observation 87d352fc-a4cf-4bae-8a33-b3b1d6a77340 · outbound

This paper cites Blazing fast PSI from improved OKVS and subfield VOLE,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Blazing fast PSI from improved OKVS and subfield VOLE,

Reference 15

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Observation 13c66dcf-424b-4678-8989-be3b5ed2287c · outbound

This paper cites PSI- Stats: Private set intersection protocols supporting secure statistical functions,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree PSI- Stats: Private set intersection protocols supporting secure statistical functions,

Reference 16

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Observation dc873130-83ae-464f-a9b0-0159a7ecd5da · outbound

This paper cites Private set operations from oblivious switching,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Private set operations from oblivious switching,

Reference 17

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Observation a733a273-dd0a-454a-8856-79ffeaab1447 · outbound

This paper cites Secure-computation-friendly pri- vate set intersection from oblivious compact graph evaluation,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Secure-computation-friendly pri- vate set intersection from oblivious compact graph evaluation,

Reference 18

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Observation 3ebfde90-d503-4eda-ae56-74a88545b538 · outbound

This paper cites CrypTFlow2: Practical 2-party secure inference,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree CrypTFlow2: Practical 2-party secure inference,

Reference 19

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Observation da32aab5-6444-42a6-a9b9-dc1ba54f59b1 · outbound

This paper cites Efficient homomorphic conversion between (ring) LWE ciphertexts,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Efficient homomorphic conversion between (ring) LWE ciphertexts,

Reference 20

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Observation e9939876-8467-4da9-b8f2-371530247971 · outbound

This paper cites Let’s stride blindfolded in a forest: Sublinear multi-client decision trees evaluation,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Let’s stride blindfolded in a forest: Sublinear multi-client decision trees evaluation,

Reference 21

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Observation 35acd80d-684c-4003-9412-7c518b92c7e1 · outbound

This paper cites XGBoost: A scalable tree boosting system,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree XGBoost: A scalable tree boosting system,

Reference 22

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Observation 490c4b7d-3145-46b2-acaf-b89c737799ab · outbound

This paper cites LightGBM: A highly efficient gradient boosting decision tree,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree LightGBM: A highly efficient gradient boosting decision tree,

Reference 23

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Observation 8bb972fb-64a0-4bf3-add8-22547b02fedc · outbound

This paper cites ABY - A framework for efficient mixed-protocol secure two-party computation,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree ABY - A framework for efficient mixed-protocol secure two-party computation,

Reference 24

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Observation 421ccd6a-d210-4721-ab9a-8471a9a3d0d5 · outbound

This paper cites Efficient multiparty protocols using circuit randomiza- tion,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Efficient multiparty protocols using circuit randomiza- tion,

Reference 25

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Observation 6fa55e9e-35f6-47ae-b182-d4ed154207c3 · outbound

This paper cites Secure computation with fixed-point numbers,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Secure computation with fixed-point numbers,

Reference 26

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Observation bafe3ec3-c7cb-4ff0-ac2c-28a5646a8f9c · outbound

This paper cites Improved garbled circuit building blocks and applications to auctions and computing minima,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Improved garbled circuit building blocks and applications to auctions and computing minima,

Reference 27

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Observation ef4ef021-2213-4378-b102-aa9ed8700f20 · outbound

This paper cites How to exchange secrets with oblivious transfer,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree How to exchange secrets with oblivious transfer,

Reference 28

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Observation 0e563c75-1b4e-4324-a8af-d3492156e1c7 · outbound

This paper cites Ferret: Fast extension for correlated OT with small communication,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Ferret: Fast extension for correlated OT with small communication,

Reference 29

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Observation 8621d785-569e-439b-aa29-8de75932e526 · outbound

This paper cites Fully homomorphic encryption from ring-lwe and security for key dependent messages,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Fully homomorphic encryption from ring-lwe and security for key dependent messages,

Reference 30

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Observation 11fe90d2-0a38-460a-9db0-765569587254 · outbound

This paper cites Security and composition of multiparty cryptographic protocols,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Security and composition of multiparty cryptographic protocols,

Reference 31

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Observation 7866bcec-ab58-4bbd-bc72-79ae8ed06689 · outbound

This paper cites Secure softmax/sigmoid for machine-learning computation,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Secure softmax/sigmoid for machine-learning computation,

Reference 32

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Observation 5b5ac08b-3c84-4d88-bb8a-82a3533195da · outbound

This paper cites SHAFT: Secure, handy, accurate, and fast transformer inference,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree SHAFT: Secure, handy, accurate, and fast transformer inference,

Reference 33

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Observation ea9dfaa5-4516-41b4-bf7a-6ecd9481bdeb · outbound

This paper cites Communication-efficient secure logistic regression,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Communication-efficient secure logistic regression,

Reference 34

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Observation f9842fe7-d6dd-4943-9f5f-62b0c23133e7 · outbound

This paper cites Are you the one to share? Secret transfer with access structure,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Are you the one to share? Secret transfer with access structure,

Reference 35

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Observation 48b062f0-e920-4233-8869-17922d902311 · outbound

This paper cites Federated transformer: Multi- party vertical federated learning on practical fuzzily linked data,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Federated transformer: Multi- party vertical federated learning on practical fuzzily linked data,

Reference 36

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Observation a0a2c926-e028-45a3-8e5f-15c2e13e588a · outbound

This paper cites Distributed learning of deep neural network over multiple agents,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Distributed learning of deep neural network over multiple agents,

Reference 37

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Observation 05a4cbd5-b7fe-4c72-bb4f-69863c8ac9aa · outbound

This paper cites Cheetah: Lean and fast secure two-party deep neu- ral network inference,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Cheetah: Lean and fast secure two-party deep neu- ral network inference,

Reference 38

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Observation 57ffe028-ad4c-4bdb-be49-b46526016c54 · outbound

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

Practical Anonymous Two-Party Gradient Boosting Decision Tree Homomorphic encryption for arithmetic of approximate numbers,

Reference 39

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Observation a76dfdb3-0ead-45a0-88c9-b8cc870dd7b4 · outbound

This paper cites Microsoft SEAL (release 4.1),.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Microsoft SEAL (release 4.1),

Reference 40

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Observation 377ab702-b8b7-4c4b-a268-462d0dc32d97 · outbound

This paper cites LIBSVM data: Classification (binary class),.

Practical Anonymous Two-Party Gradient Boosting Decision Tree LIBSVM data: Classification (binary class),

Reference 41

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Observation 9b935c78-2de2-41d8-92a0-d1fb722ae57e · outbound

This paper cites eXtreme gradient boosting,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree eXtreme gradient boosting,

Reference 42

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Observation 70d06686-48ff-4dea-97d5-2d6ca7f92c20 · outbound

This paper cites Efficient permu- tation correlations and batched random access for two-party compu- tation,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Efficient permu- tation correlations and batched random access for two-party compu- tation,

Reference 43

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Observation 9c81380a-aa8e-4cc5-8c3e-e3e8db8a73c3 · outbound

This paper cites Private matching for compute,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Private matching for compute,

Reference 44

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Observation 391a6bcf-01d6-46f7-8d32-71d3023278f8 · outbound

This paper cites Can you find the one for me?.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Can you find the one for me?

Reference 45

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Observation 3b8e15d6-4434-499f-a6eb-aa083374c793 · outbound

This paper cites Privacy preserving data mining,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Privacy preserving data mining,

Reference 46

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Observation 9a3ad2e7-bb26-4d3a-b426-3eea0e4eed05 · outbound

This paper cites Understanding privacy risk of publishing decision trees,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Understanding privacy risk of publishing decision trees,

Reference 47

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Observation 7fc31a0f-9e22-4aa0-be3c-16d5f44b107e · outbound

This paper cites SecureXGB: A secure and efficient multi-party protocol for vertical federated XGBoost,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree SecureXGB: A secure and efficient multi-party protocol for vertical federated XGBoost,

Reference 48

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Observation 27459112-b034-44b8-b2ed-51b48667de15 · outbound

This paper cites Efficient de- cision tree training with new data structure for secure multi-party computation,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Efficient de- cision tree training with new data structure for secure multi-party computation,

Reference 49

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Observation 596073a2-333e-48b1-9e62-cc2aeab91731 · outbound

This paper cites Securely training decision trees efficiently,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Securely training decision trees efficiently,

Reference 50

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Observation 2b61ce1d-96ec-4eca-8f97-d70de0dbbbfc · outbound

This paper cites Ents: An efficient three-party training framework for decision trees by communication optimization,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Ents: An efficient three-party training framework for decision trees by communication optimization,

Reference 51

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Observation c9bb6793-d2dc-4711-a374-d92a0cd5bfa4 · outbound

This paper cites SecureBoost: A lossless federated learning framework,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree SecureBoost: A lossless federated learning framework,

Reference 52

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Observation 5788c7cf-59cf-484e-a8e8-f5affbfa2156 · outbound

This paper cites VF2Boost: Very fast vertical federated gradient boosting for cross- enterprise learning,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree VF2Boost: Very fast vertical federated gradient boosting for cross- enterprise learning,

Reference 53

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Observation aaba8afe-26f3-4337-a61f-20ac066f710e · outbound

This paper cites Label inference attacks against vertical federated learning,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Label inference attacks against vertical federated learning,

Reference 54

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Observation 536ce304-968f-45e2-bec1-23e30c2e21d5 · outbound

This paper cites Eliminating Label Leakage in Tree-Based Vertical Federated Learning.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Eliminating Label Leakage in Tree-Based Vertical Federated Learning

Reference 55

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Observation d3196542-603d-4a06-b9dc-d96c0948c077 · outbound

This paper cites Practical secure decision tree learning in a teletreatment application,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Practical secure decision tree learning in a teletreatment application,

Reference 56

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Observation 56c486b2-718f-4e63-988a-63d0a7c84600 · outbound

This paper cites Secure training of decision trees with continuous attributes,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Secure training of decision trees with continuous attributes,

Reference 57

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Observation 5abcb33d-1e9f-4eb8-9800-969cef7b8b33 · outbound

This paper cites SiGBDT: Large-scale gradient boosting decision tree training via function secret sharing,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree SiGBDT: Large-scale gradient boosting decision tree training via function secret sharing,

Reference 58

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Observation 3923713d-e690-4e14-90cd-597190413791 · outbound

This paper cites NodeGuard: A highly efficient two-party computation framework for training large-scale gradient boosting decision tree,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree NodeGuard: A highly efficient two-party computation framework for training large-scale gradient boosting decision tree,

Reference 59

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Observation 5a1c7aa1-ccfe-4f6d-af9e-a7c4cfb2fe43 · outbound

This paper cites SoK: Privacy-preserving collaborative tree-based model learning,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree SoK: Privacy-preserving collaborative tree-based model learning,

Reference 60

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Observation 996cfd1b-832f-45dd-b78d-f2428e659d66 · outbound

This paper cites Vertical Federated Learning without Revealing Intersection Membership.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Vertical Federated Learning without Revealing Intersection Membership

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.980090Z

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Observation bad7ad0a-5a39-45fa-854c-955d4213967f · outbound

This paper cites OpenVFL: A vertical federated learn- ing framework with stronger privacy-preserving,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree OpenVFL: A vertical federated learn- ing framework with stronger privacy-preserving,

Reference 62

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Observation d488a5aa-c2a0-48f8-ba51-89798748ef05 · outbound

This paper cites Improved multiplication triple generation over rings via RLWE-based AHE,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Improved multiplication triple generation over rings via RLWE-based AHE,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-29T16:46:43.890086Z

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Observation af7b80a3-6dc5-452c-91e4-ea78585f82b5 · outbound

This paper cites (Leveled) fully ho- momorphic encryption without bootstrapping,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree (Leveled) fully ho- momorphic encryption without bootstrapping,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-29T16:46:43.890086Z

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Unavailable: canonical work link unavailable.

Observation abc04db9-ed5c-4dfd-9e37-9563d3fee350 · outbound

This paper cites Linear private set union from multi-query reverse private membership test,.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Linear private set union from multi-query reverse private membership test,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-29T16:46:43.890086Z

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Observation d02502ea-b94a-44dc-a818-eacb48416979 · outbound

This paper cites ThenScomputes⟨b ∗⟩B 0 =⟨b ∗⟩B 1 ⊕fM0[z∗B+u ∗]; elseSplays the sender ofF OT with input of the XOR of randomly chosenr(=⟨b ∗⟩B 0 ) and⟨ fM1⟩B 0 as in Line 8.

Practical Anonymous Two-Party Gradient Boosting Decision Tree ThenScomputes⟨b ∗⟩B 0 =⟨b ∗⟩B 1 ⊕fM0[z∗B+u ∗]; elseSplays the sender ofF OT with input of the XOR of randomly chosenr(=⟨b ∗⟩B 0 ) and⟨ fM1⟩B 0 as in Line 8

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-29T16:46:43.890086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cde752d-12a2-4d92-a04e-a518ec74cef7 · outbound

This paper cites an unresolved cited work.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-29T16:46:43.890086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81a14e54-7086-4292-b090-35fd8b355fef · outbound

This paper cites an unresolved cited work.

Practical Anonymous Two-Party Gradient Boosting Decision Tree Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-29T16:46:43.890086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 460a3d29-4e4e-4e0a-b9af-6ea4e86bf4c1 · outbound

This paper cites To prove the simulation is indistinguishable from the real protocol, we consider the following hybrids.

Practical Anonymous Two-Party Gradient Boosting Decision Tree To prove the simulation is indistinguishable from the real protocol, we consider the following hybrids

Reference 69

Resolution: unresolved; no resolver link, observed 2026-06-29T16:46:43.890086Z

Source-reported events for the cited work

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