Paper Citation Record · LEDGER

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage

As of 13 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2502.02913.

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

pith.paper-citation-record.v1
2502.02913 v4

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:44:58.551571Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

16 of 16 outbound references displayed

verified exact2
verified fuzzy9
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b4e62d67-426f-42ad-b843-e0039352dfbf · outbound

This paper cites B., Mironov, I., Talwar, K., and Zhang, L.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage B., Mironov, I., Talwar, K., and Zhang, L

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.762941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a444f083-9a12-4104-bbde-7fdf03585f34 · outbound

This paper cites Comprehen- sive privacy analysis of deep learning.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Comprehen- sive privacy analysis of deep learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.728327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 72b8ebef-94b3-4d8e-bcdc-269b6ce4db1d · outbound

This paper cites Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T10:44:58.529061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38d756df-0a18-4b97-8f70-93a424a9cd5e · outbound

This paper cites Mem- bership inference attacks against machine learning mod- els.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Mem- bership inference attacks against machine learning mod- els

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.704869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89ce4689-67f5-49a8-8fc4-1a83cc152e1f · outbound

This paper cites Variational model inversion attacks.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Variational model inversion attacks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.681386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2bc2f188-1f78-47a4-9224-5e9df346b745 · outbound

This paper cites Settings of MINE Network.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Settings of MINE Network

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.669432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 94eb60c3-3b41-48bc-917a-a33f33187f98 · outbound

This paper cites Detecting adversarial examples on deep neural networks with mutual information neural esti- mation.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Detecting adversarial examples on deep neural networks with mutual information neural esti- mation

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.739945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6dc60f01-a935-4ec5-9c92-931e75d362a5 · outbound

This paper cites MINE: Mutual Information Neural Estimation.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage MINE: Mutual Information Neural Estimation

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-09T10:44:58.495070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74014975-f5ef-43c8-9fa3-79e9802c51d1 · outbound

This paper cites E., Yu, L., and Wei, W.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage E., Yu, L., and Wei, W

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.693122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 86e98dff-6722-478c-bd1d-b9c03e90e138 · outbound

This paper cites A., Tramer, F., Carlini, N., and Paper- not, N.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage A., Tramer, F., Carlini, N., and Paper- not, N

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.751702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a6751d79-d943-43d1-8546-2f4f448d6cfc · outbound

This paper cites Adversarial Machine Learning at Scale.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Adversarial Machine Learning at Scale

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T10:44:58.521159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5edd8ac-3f06-4b77-8712-0daa20620909 · outbound

This paper cites V ., Krpalkova, L., Riordan, D., and Walsh, J.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage V ., Krpalkova, L., Riordan, D., and Walsh, J

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:44:58.716246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 446be18f-0d92-485e-adfd-ba9b0fbc4ac7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T10:44:58.503867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf57f052-ece7-47ff-bb39-050c93273a83 · outbound

This paper cites Intermediate Outputs Are More Sensitive Than You Think.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Intermediate Outputs Are More Sensitive Than You Think

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-09T10:44:58.621232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 381e7552-db1d-456a-916c-2a101c548c69 · outbound

This paper cites Modelling and Quantifying Membership Information Leakage in Machine Learning.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage Modelling and Quantifying Membership Information Leakage in Machine Learning

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-09T10:44:58.637443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9259e078-b24d-4a83-8f2a-2fb6509493fd · outbound

This paper cites iDLG: Improved Deep Leakage from Gradients.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage iDLG: Improved Deep Leakage from Gradients

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T10:44:58.547558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.