Paper Citation Record · LEDGER

Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2306.08223.

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

pith.paper-citation-record.v1
2306.08223 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:44:46.758140Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T09:26:51.586577Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f55a920c-c8b8-4afd-bb6f-4236ad28d8ce · inbound

ConfusionPrompt: Practical Private Inference for Online Large Language Models cites this paper.

ConfusionPrompt: Practical Private Inference for Online Large Language Models Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:58:54.880420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07f37d3d-983a-4cf7-92eb-2cc9c67672b3 · inbound

LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice cites this paper.

LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T18:53:52.490026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d455471c-2036-45b4-bb2b-a6343aeaef4d · inbound

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks cites this paper.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 181

Resolution: unresolved; no resolver link, observed 2026-08-16T10:44:46.758140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af45f448-beb8-4cb9-9ad1-ae6416004be6 · inbound

Preserving Privacy and Utility in LLM-Based Product Recommendations cites this paper.

Preserving Privacy and Utility in LLM-Based Product Recommendations Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T04:35:58.171847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21335cf8-5d49-4f0d-95b6-cb6397575c9d · inbound

LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance cites this paper.

LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:01.404609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6cd725d-dfc7-40bd-9882-6502403b4226 · inbound

What Makes a Good Natural Language Prompt? cites this paper.

What Makes a Good Natural Language Prompt? Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:50:35.882497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59bc73fb-ddaa-4ddf-bc29-127a67dca06a · inbound

Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models cites this paper.

Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:06:30.152209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d57dcf78-8ac4-41b5-8c66-1a42e403e862 · inbound

Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation cites this paper.

Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:46:33.059082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ec34365-5209-45dd-b373-b46984fa0e58 · inbound

SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models cites this paper.

SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T09:26:51.587809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.