Paper Citation Record · LEDGER

Intermediate Outputs Are More Sensitive Than You Think

As of 12 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2412.00696.

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

pith.paper-citation-record.v1
2412.00696 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:10:28.281650Z

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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:44:58.516688Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T10:44:58.615067Z

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy8
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6ce2fa5-4e1f-43c4-87ea-b9691016205a · outbound

This paper cites Deep learning with differential privacy.

Intermediate Outputs Are More Sensitive Than You Think Deep learning with differential privacy

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T05:10:28.176278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09c5292a-67e5-47c2-820a-6b9e307cbaca · outbound

This paper cites Privacy in Deep Learning: A Survey.

Intermediate Outputs Are More Sensitive Than You Think Privacy in Deep Learning: A Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T05:10:28.210044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3866f09-9dfa-4334-939f-ba7e07e6d472 · outbound

This paper cites Sok: Security and privacy in machine learning.

Intermediate Outputs Are More Sensitive Than You Think Sok: Security and privacy in machine learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:10:28.586440Z

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 3d186aa1-dce6-4a22-9c1b-34ce22f85c45 · outbound

This paper cites Membership inference attacks against machine learning models.

Intermediate Outputs Are More Sensitive Than You Think Membership inference attacks against machine learning models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T05:10:28.238903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52d49ddb-b364-408f-ae7a-c2efd9ae21e4 · outbound

This paper cites Enhanced membership inference attacks against machine learning models.

Intermediate Outputs Are More Sensitive Than You Think Enhanced membership inference attacks against machine learning models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:10:28.471513Z

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 54bfcadb-33ec-410c-9eeb-e660a39b5a41 · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting.

Intermediate Outputs Are More Sensitive Than You Think Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:10:28.437220Z

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 e7fff4b4-bbed-4b4f-a796-24afb964d78e · outbound

This paper cites Demystifying membership inference attacks in machine learning as a service.

Intermediate Outputs Are More Sensitive Than You Think Demystifying membership inference attacks in machine learning as a service

Reference 1969

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:10:28.541014Z

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 1b8051bf-5ec7-4c14-9294-4fdade29159a · outbound

This paper cites Membership inference attacks from first principles.

Intermediate Outputs Are More Sensitive Than You Think Membership inference attacks from first principles

Reference 1975

Resolution: unresolved; no resolver link, observed 2026-08-12T05:10:28.192045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d373497-9454-4d6b-87cf-e19c84ed49d0 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks.

Intermediate Outputs Are More Sensitive Than You Think Distillation as a defense to adversarial perturbations against deep neural networks

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:10:28.604933Z

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 bd8bf9d6-c785-47ef-b97d-6d3009a6a7ae · outbound

This paper cites Deep learning for computer vision: A brief review.

Intermediate Outputs Are More Sensitive Than You Think Deep learning for computer vision: A brief review

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:10:28.501634Z

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 820e317d-1030-4ea6-88d7-4dcdfc5083ca · outbound

This paper cites Towards better understanding of gradient-based attribution methods for Deep Neural Networks.

Intermediate Outputs Are More Sensitive Than You Think Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T05:10:28.186094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a61a3e6-f66e-4655-8df8-eebe53f849b3 · outbound

This paper cites Optimality of the johnson-lindenstrauss lemma.

Intermediate Outputs Are More Sensitive Than You Think Optimality of the johnson-lindenstrauss lemma

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:10:28.647462Z

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 03ac0a6c-9428-4d19-a5bf-e1f6f096c282 · outbound

This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.

Intermediate Outputs Are More Sensitive Than You Think Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:10:28.624532Z

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 ecda1d27-7862-4473-a814-9b686f3a873d · outbound

This paper cites A Survey on Gradient Inversion: Attacks, Defenses and Future Directions.

Intermediate Outputs Are More Sensitive Than You Think A Survey on Gradient Inversion: Attacks, Defenses and Future Directions

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T05:10:28.281650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e5cbc48-fedd-426d-894a-7dff7707e176 · outbound

This paper cites Robust Learning with Jacobian Regularization.

Intermediate Outputs Are More Sensitive Than You Think Robust Learning with Jacobian Regularization

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T05:10:28.197952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage cites this paper.

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.