Paper Citation Record · LEDGER

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?

As of 19 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2504.21036.

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

pith.paper-citation-record.v1
2504.21036 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:56:06.001502Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

31 of 31 outbound references displayed

verified exact0
verified fuzzy16
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1344fce5-15fb-41c2-bb03-109dda75ad04 · outbound

This paper cites In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.861762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeb9e1b5-e4a0-42a0-84f0-476f2d531b9c · outbound

This paper cites In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.508446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9b49c36-a8ed-4152-9a2b-93905dafc913 · outbound

This paper cites Zero redundancy distributed learning with differential privacy.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Zero redundancy distributed learning with differential privacy

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T05:56:06.176356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b72fe04f-9aca-4c95-8d64-d2b568253f3d · outbound

This paper cites In: Workshop on Trustworthy and Socially Respon- sible Machine Learning, NeurIPS 2022 (2022).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Workshop on Trustworthy and Socially Respon- sible Machine Learning, NeurIPS 2022 (2022)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.491707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1561a3d0-109d-43f0-970a-979a555871f5 · outbound

This paper cites In: Proceedings of the 40th International Conference on Machine Learning (2023).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Proceedings of the 40th International Conference on Machine Learning (2023)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.473811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9d84da6-b42e-48d2-8842-e5a2b1813ad1 · outbound

This paper cites In: International Conference on Machine Learning.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: International Conference on Machine Learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.458283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b5aebe2c-e041-4a30-9b20-005e79d8d737 · outbound

This paper cites an unresolved cited work.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:56:06.443245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2e6992ad-f0c2-4878-a6c4-fef28b603caf · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.898647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f324b8e-8195-42ca-91a8-6521ca464dda · outbound

This paper cites In: International colloquium on automata, lan- guages, and programming.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: International colloquium on automata, lan- guages, and programming

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.425715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 59173e20-2c1e-4269-8b36-3371b84c3b99 · outbound

This paper cites In: 2019 IEEE International Conference on Data Mining.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: 2019 IEEE International Conference on Data Mining

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.410113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47ed0543-fa60-4c8d-8317-db25715210e5 · outbound

This paper cites Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.911797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8081e4e5-f360-4e8d-95c1-c08fe72e7ec0 · outbound

This paper cites Transactions on Machine Learning Research (2024).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Transactions on Machine Learning Research (2024)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.916433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f64f552b-a290-4ed7-97ba-e4340e1e8690 · outbound

This paper cites In: International Con- ference on Learning Representations (2022).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: International Con- ference on Learning Representations (2022)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.920752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cc571b6-0d81-468c-a431-cd115d822cc2 · outbound

This paper cites Membership Inference Attack Susceptibility of Clinical Language Models.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Membership Inference Attack Susceptibility of Clinical Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.925061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2fc7351-b1b1-4ee2-b91e-6bd3ba9dc4a8 · outbound

This paper cites In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.929758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7030c0c0-0ae0-4935-a95a-d2ecbec84b79 · outbound

This paper cites In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Pro- cessing.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Pro- cessing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.366567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2db07e0e-6eff-4e56-b1a4-e782d8b912d5 · outbound

This paper cites arXiv preprint arXiv:2305.06212 (2023) 18 Hao Du et al.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? arXiv preprint arXiv:2305.06212 (2023) 18 Hao Du et al

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.939118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 603e90d5-9b09-4ba2-945d-c805da88fce2 · outbound

This paper cites In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.350495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 21552c46-1109-49a4-bfdc-9a312d8fbb84 · outbound

This paper cites AI Open5, 208–215 (2024).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? AI Open5, 208–215 (2024)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.334669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 33684c8e-ff6e-4988-a677-9a69130dd474 · outbound

This paper cites In: 2023 IEEE Symposium on Security and Privacy.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: 2023 IEEE Symposium on Security and Privacy

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.320140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aecaee86-e523-4162-8d7c-16509997507f · outbound

This paper cites https://github.com/ huggingface/peft (2022).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? https://github.com/ huggingface/peft (2022)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.306607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03809114-f624-46d6-a4e1-26600f2f8059 · outbound

This paper cites an unresolved cited work.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:56:06.293836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 99e618dc-a28e-432d-9728-147e10b74791 · outbound

This paper cites an unresolved cited work.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.964425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46d61b07-f5f7-4a6a-b61e-3a3ba170c2aa · outbound

This paper cites In: Goldberg, Y., Kozareva, Z., Zhang, Y.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Goldberg, Y., Kozareva, Z., Zhang, Y

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.270588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 76d85f8a-a4f7-4b63-a399-16ecf36f7d35 · outbound

This paper cites In: EMNLP.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: EMNLP

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.256166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7e85001c-d341-4ee0-a12d-6434db01e4be · outbound

This paper cites In: The Thirteenth International Conference on Learning Representations (2025).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: The Thirteenth International Conference on Learning Representations (2025)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.241134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6fc7ac41-9e8e-4d61-9226-9337cc2de712 · outbound

This paper cites an unresolved cited work.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.983104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c225c6c-2ec7-4308-968d-abdedfa82988 · outbound

This paper cites https://github.com/kingoflolz/mesh-transformer-jax (May 2021).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? https://github.com/kingoflolz/mesh-transformer-jax (May 2021)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.216022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8f2dc6dd-0f4a-4696-989f-f63a2e7f664c · outbound

This paper cites In: Proceedings ofthe2020ConferenceonEmpiricalMethodsinNaturalLanguageProcessing:Sys- tem Demonstrations.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: Proceedings ofthe2020ConferenceonEmpiricalMethodsinNaturalLanguageProcessing:Sys- tem Demonstrations

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:56:06.200569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5716c7a4-c927-494b-8876-b8efd47c73f2 · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:05.996779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82d0a8ac-758b-4976-8f99-905c5550a6b8 · outbound

This paper cites In: NIPS (2015).

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks? In: NIPS (2015)

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:06.001502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.