Paper Citation Record · LEDGER

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting

As of 7 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2602.07126.

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

pith.paper-citation-record.v1
2602.07126 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T06:30:07.950434Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T01:20:47.445222Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-13T01:22:01.521729Z

Reference resolution

49 of 49 outbound references displayed

verified exact2
verified fuzzy46
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a64f0b9a-6f1c-40d1-8cf8-d1728d498c7e · outbound

This paper cites what do you want from theory alone?.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting what do you want from theory alone?

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.593953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dfd05a69-e62b-4c25-818d-25e6526f9b1d · outbound

This paper cites Language models are realistic tabular data generators.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Language models are realistic tabular data generators

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.583603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a52f5857-7c85-40bb-824f-c79317c20f3c · outbound

This paper cites How attentive are graph attention networks? InInternational Confer- ence on Learning Representations.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting How attentive are graph attention networks? InInternational Confer- ence on Learning Representations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.596500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a4715c2f-4b1e-45ac-82ff-ada2fa9fdfa8 · outbound

This paper cites Terzis, and Florian Tramèr.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Terzis, and Florian Tramèr

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.578272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4ba6b46b-a2d2-4da4-9b18-7ec50a4a18ec · outbound

This paper cites Integrated public use microdata series, international: Version 7.3.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Integrated public use microdata series, international: Version 7.3

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.580831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 41d53a2c-7b21-4081-8132-1d8e12470d1a · outbound

This paper cites Gan-leaks: A taxonomy of membership inference at- tacks against generative models.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Gan-leaks: A taxonomy of membership inference at- tacks against generative models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.601322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2a90f853-6e67-4a39-8e6a-41f2150ef18a · outbound

This paper cites From data mining to knowledge discovery in databases.AI Magazine, 17(3):37, Mar.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting From data mining to knowledge discovery in databases.AI Magazine, 17(3):37, Mar

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.606415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dbf8a24e-944c-44c1-9859-432f2251b999 · outbound

This paper cites Relational deep learning: Graph representation learning on relational databases.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Relational deep learning: Graph representation learning on relational databases

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.598952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e476c5c5-2254-4241-bf0b-aa66377056ca · outbound

This paper cites Position: Relational deep learning - graph representation learning on rela- tional databases.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Position: Relational deep learning - graph representation learning on rela- tional databases

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.603922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8f99502a-9388-49a8-804f-1371cbb4d722 · outbound

This paper cites Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.591472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 24e02ce5-cabf-438c-b7a5-0c05badd259a · outbound

This paper cites Row conditional-tgan for generating synthetic relational databases.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Row conditional-tgan for generating synthetic relational databases

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.586441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9dda110b-3a2f-4829-9172-84f2d782a490 · outbound

This paper cites Lost in the averages: A new specific setup to evaluate membership inference attacks against machine learning models.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Lost in the averages: A new specific setup to evaluate membership inference attacks against machine learning models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.588934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 796881f8-e928-4bf7-86ed-73498cb70792 · outbound

This paper cites Logan: Membership inference at- tacks against generative models.Proceedings on Pri- vacy Enhancing Technologies, 2019:133 – 152.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Logan: Membership inference at- tacks against generative models.Proceedings on Pri- vacy Enhancing Technologies, 2019:133 – 152

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.633388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e894a425-119a-4e40-9597-03bcf553fccd · outbound

This paper cites Monte carlo and reconstruction membership in- ference attacks against generative models.Proceedings on Privacy Enhancing Technologies, 2019:232 – 249.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Monte carlo and reconstruction membership in- ference attacks against generative models.Proceedings on Privacy Enhancing Technologies, 2019:232 – 249

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.694555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3f870e8c-545b-4a1d-991d-427a9e42a696 · outbound

This paper cites TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:30:41.462964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b0013b23-7fe1-4d9a-9358-2df013ed1b97 · outbound

This paper cites Heterogeneous graph transformer.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Heterogeneous graph transformer

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.690840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ea847b38-3ede-4485-96b9-35b41136880a · outbound

This paper cites MIDST Challenge: Membership In- ference over Diffusion-models-based Synthetic Tab- ular data.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting MIDST Challenge: Membership In- ference over Diffusion-models-based Synthetic Tab- ular data

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.688099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5d54ff57-5291-4a81-bf77-b2eee27fcec5 · outbound

This paper cites Tabddpm: Modelling tabular data with diffusion models.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Tabddpm: Modelling tabular data with diffusion models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.685445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c131ed3d-cabd-40cc-8e2c-78ac66375f4c · outbound

This paper cites Self- attention graph pooling.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Self- attention graph pooling

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.610847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9fc0adb5-e546-4337-92b9-d0eaf74a2bbf · outbound

This paper cites Gunter, and Kai Chen.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Gunter, and Kai Chen

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.608605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e4c89d0e-1dcc-4f0f-878b-a6537ace2538 · outbound

This paper cites Blanco, and María Amparo Vila.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Blanco, and María Amparo Vila

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.643853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 22aa4d6b-7030-4ce6-b232-23df07784901 · outbound

This paper cites Springer Nature Switzerland.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Springer Nature Switzerland

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.682978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5cfcc7eb-6962-469e-8a69-a6cc4a1cf6e0 · outbound

This paper cites Tabular transformers for modeling multivariate time series.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Tabular transformers for modeling multivariate time series

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.680500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 441ee5b5-fc6f-4075-959c-09eba0602fe0 · outbound

This paper cites Airline loyalty campaign program impact on flights.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Airline loyalty campaign program impact on flights

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.653198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ec49e949-e464-4909-a829-43e5917012f8 · outbound

This paper cites Clavaddpm: multi-relational data synthesis with cluster-guided diffusion models.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Clavaddpm: multi-relational data synthesis with cluster-guided diffusion models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.663151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e52e12d7-673e-4bae-82dd-d53316e8621c · outbound

This paper cites The synthetic data vault.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting The synthetic data vault

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.627030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 51e0cb68-adf0-4962-9dfc-7aa3173157a4 · outbound

This paper cites Relbench: A benchmark for deep learning on relational databases.Advances in Neural Information Processing Systems, 37:21330–21341.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Relbench: A benchmark for deep learning on relational databases.Advances in Neural Information Processing Systems, 37:21330–21341

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.664290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f02b591e-1a95-4fab-9015-cff0ee312ba0 · outbound

This paper cites White-box vs black-box: Bayes optimal strategies for membership inference.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting White-box vs black-box: Bayes optimal strategies for membership inference

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.632972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8e406cdd-4d5e-4f23-a7de-24ba691b755d · outbound

This paper cites Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.703718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 51481d64-58b5-4396-947f-5b481284c9c2 · outbound

This paper cites Shokri, M.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Shokri, M

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.672299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3e3fec6c-2861-469c-8e9f-70a3725f90b4 · outbound

This paper cites REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:30:41.457030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3d5853f6-9616-4954-b166-d3d676a7f463 · outbound

This paper cites Synthetic data – anonymisation groundhog day.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Synthetic data – anonymisation groundhog day

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.670047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e4efcbf2-80f3-43c5-8491-769b38afcada · outbound

This paper cites Autodiff: combining auto-encoder and diffusion model for tabular data syn- thesizing.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Autodiff: combining auto-encoder and diffusion model for tabular data syn- thesizing

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.677746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e36e4918-1e0c-4795-b117-cd6f4372882f · outbound

This paper cites Membership inference attacks against synthetic data through overfitting detection.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Membership inference attacks against synthetic data through overfitting detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.667921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6651c355-ec3f-42c1-994e-3f32d13fa9a4 · outbound

This paper cites Airbnb recruiting: New user bookings.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Airbnb recruiting: New user bookings

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.665527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dcaad32f-91dc-41f4-8ed3-55b3846359d2 · outbound

This paper cites Heterogeneous graph attention network.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Heterogeneous graph attention network

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.706927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6b36353f-d780-424f-99fa-e50e1b9e8e45 · outbound

This paper cites Synth-mia: A testbed for auditing privacy leak- age in tabular data synthesis.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Synth-mia: A testbed for auditing privacy leak- age in tabular data synthesis

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.697789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 705d5c4a-9737-43e6-bbfc-efffe69d6746 · outbound

This paper cites Data plagiarism index: Characterizing the privacy risk of data- copying in tabular generative models.KDD- Generative AI Evaluation Workshop.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Data plagiarism index: Characterizing the privacy risk of data- copying in tabular generative models.KDD- Generative AI Evaluation Workshop

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.674762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b545312e-b4be-4ce5-900e-9505926c0857 · outbound

This paper cites Privacy auditing synthetic data release through local likelihood attacks.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Privacy auditing synthetic data release through local likelihood attacks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.624515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3bc83ec6-62bb-4d12-80bd-60852528bbbe · outbound

This paper cites On the importance of diffi- culty calibration in membership inference attacks.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting On the importance of diffi- culty calibration in membership inference attacks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.660255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6f9f4b3e-34ab-4076-94b4-5e3c43ac992a · outbound

This paper cites Winning the midst challenge: New membership infer- ence attacks on diffusion models for tabular data synthe- sis.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Winning the midst challenge: New membership infer- ence attacks on diffusion models for tabular data synthe- sis

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.649843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ab9b3f31-7762-438e-b5ab-7241654509c8 · outbound

This paper cites Modeling tabular data using conditional gan.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Modeling tabular data using conditional gan

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.657615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8736e2c4-3ff5-43e5-8ccd-71c34dc57e1c · outbound

This paper cites Simple and efficient heterogeneous graph neural network.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Simple and efficient heterogeneous graph neural network

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.700806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation acdfb01d-09d4-40f5-92e1-688a38f5292b · outbound

This paper cites Enhanced membership inference attacks against machine learn- ing models.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Enhanced membership inference attacks against machine learn- ing models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.629868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation df009c96-0622-476e-96aa-2078dffba5aa · outbound

This paper cites Anonymization through data synthe- sis using generative adversarial networks (ads-gan).

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Anonymization through data synthe- sis using generative adversarial networks (ads-gan)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.621322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fd3a6cfd-f30a-457c-86bd-ea9203bc1e50 · outbound

This paper cites PATE-GAN: Generating synthetic data with differential privacy guarantees.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting PATE-GAN: Generating synthetic data with differential privacy guarantees

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.666968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8d060313-382d-4519-9a5a-41908bb22ad9 · outbound

This paper cites Low- cost high-power membership inference attacks.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Low- cost high-power membership inference attacks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.651272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 63fd7530-0ac4-4b57-a758-727ebe9299ab · outbound

This paper cites an unresolved cited work.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-05-16T06:30:41.647410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d8fa73ba-7694-4122-a311-c49f91835e72 · outbound

This paper cites Mixed-type tabular data synthesis with score-based diffusion in la- tent space.

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting Mixed-type tabular data synthesis with score-based diffusion in la- tent space

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T06:30:41.642573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

Observation 33e9adaf-fcbc-4f03-aaf3-90d5cd24515c · inbound

FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models cites this paper.

FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-13T01:22:01.523652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.