Paper Citation Record · LEDGER

Harnessing large-language models to generate private synthetic text

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2306.01684.

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

pith.paper-citation-record.v1
2306.01684 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 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 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:55:18.168118Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7c03d306-930e-407f-9ae7-c4e687f59c51 · inbound

ShieldGemma: Generative AI Content Moderation Based on Gemma cites this paper.

ShieldGemma: Generative AI Content Moderation Based on Gemma Harnessing large-language models to generate private synthetic text

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:17:39.515674Z

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 483f2c46-0785-4b07-8c37-a8775c89a0a9 · inbound

Bridging the Data Provenance Gap Across Text, Speech and Video cites this paper.

Bridging the Data Provenance Gap Across Text, Speech and Video Harnessing large-language models to generate private synthetic text

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-11T12:18:39.153956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a12fedf7-738b-4933-bcab-b1e138a89189 · inbound

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data? cites this paper.

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data? Harnessing large-language models to generate private synthetic text

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T15:07:10.062722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5403b03-8505-4786-b3b0-77c66e1024c9 · inbound

Language Agents as Digital Representatives in Collective Decision-Making cites this paper.

Language Agents as Digital Representatives in Collective Decision-Making Harnessing large-language models to generate private synthetic text

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T21:50:03.083411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53d1b9f4-4d4a-44e4-a7e0-74e57c93eef7 · inbound

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data cites this paper.

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data Harnessing large-language models to generate private synthetic text

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T11:55:18.168118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09f08d13-b70d-4560-b0a0-7b0baf288986 · inbound

Less is More: Adaptive Coverage for Synthetic Training Data cites this paper.

Less is More: Adaptive Coverage for Synthetic Training Data Harnessing large-language models to generate private synthetic text

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:51:08.052639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94155881-a85d-42a4-83ce-3b2a4585585a · inbound

POPri: Private Federated Learning using Preference-Optimized Synthetic Data cites this paper.

POPri: Private Federated Learning using Preference-Optimized Synthetic Data Harnessing large-language models to generate private synthetic text

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:09:46.548155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3f49611-9740-4f49-a4de-3a13d22dc6f1 · inbound

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures cites this paper.

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures Harnessing large-language models to generate private synthetic text

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T04:28:00.494345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2d73466-879d-4ae6-908b-cae8e3966e9f · inbound

Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset Generation & Smoke-Tests for Continuous LLM Evaluation cites this paper.

Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset Generation & Smoke-Tests for Continuous LLM Evaluation Harnessing large-language models to generate private synthetic text

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:49.113008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66ce17f6-322d-475e-a918-552dcd5d40a0 · inbound

Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications cites this paper.

Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications Harnessing large-language models to generate private synthetic text

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:03.541612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0247f1d-b273-47c3-a4b0-edf0355478e9 · inbound

Clustering and Median Aggregation Improve Differentially Private Inference cites this paper.

Clustering and Median Aggregation Improve Differentially Private Inference Harnessing large-language models to generate private synthetic text

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:47:28.344559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bc14da6-e758-46d7-a4f7-a5883708b2d2 · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Harnessing large-language models to generate private synthetic text

Reference 170

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:35.982399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 821254cd-ed7d-4d5a-8d6a-d97e94e2064b · inbound

Term2Note: Synthesising Differentially Private Clinical Notes from Medical Terms cites this paper.

Term2Note: Synthesising Differentially Private Clinical Notes from Medical Terms Harnessing large-language models to generate private synthetic text

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:48.130965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2b551c5-8ada-4b67-be30-ee7243b40bb7 · inbound

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy cites this paper.

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy Harnessing large-language models to generate private synthetic text

Reference 133

Resolution: unresolved; no resolver link, observed 2026-08-03T18:52:57.840718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec03c3c5-8d35-4e19-b336-ff6452eda164 · inbound

MAPLE: Metadata Augmented Private Language Evolution cites this paper.

MAPLE: Metadata Augmented Private Language Evolution Harnessing large-language models to generate private synthetic text

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T05:59:27.385496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df3c6153-26d4-42ef-ae6e-1d71417b5060 · inbound

DP-OPD: Differentially Private On-Policy Distillation for Language Models cites this paper.

DP-OPD: Differentially Private On-Policy Distillation for Language Models Harnessing large-language models to generate private synthetic text

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T23:15:48.459104Z

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 99168218-17d3-453b-9e4f-f022008a755a · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion Harnessing large-language models to generate private synthetic text

Reference 121

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:56:05.828261Z

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 3f53ba47-d4db-4138-a893-fe8ebcb2bf36 · inbound

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models cites this paper.

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models Harnessing large-language models to generate private synthetic text

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:38:19.403162Z

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 d9b100ec-674d-4b5e-a47e-be41e2eaf72f · inbound

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities? cites this paper.

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities? Harnessing large-language models to generate private synthetic text

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-28T15:42:21.721786Z

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 45279419-782e-4f38-affa-1fbe26a26c7b · inbound

Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy cites this paper.

Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy Harnessing large-language models to generate private synthetic text

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:47:27.622065Z

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 88157295-aba4-449a-829f-e8b4d9a7e390 · inbound

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data cites this paper.

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data Harnessing large-language models to generate private synthetic text

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T11:15:50.068311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49d52a7d-a37f-4e2d-bdef-e1d0aff51826 · inbound

Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation cites this paper.

Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation Harnessing large-language models to generate private synthetic text

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T16:26:23.697959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.