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Paper Citation Record · LEDGER

Clustering and Median Aggregation Improve Differentially Private Inference

As of 9 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2506.04566.

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

pith.paper-citation-record.v1
2506.04566 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:47:28.383142Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T06:51:03.385016Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T06:52:07.859138Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact5
  • verified fuzzy8
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 15acf7bb-249c-4e7a-b0e0-5acf599975fe · outbound

This paper cites an unresolved cited work.

Clustering and Median Aggregation Improve Differentially Private Inference Unresolved cited work

Reference 1

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unresolved
raw_fallback, observed 2026-08-07T10:47:28.633447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5325b1a5-62b3-4582-ae2d-8d63bef61075 · outbound

This paper cites ```" for PT,.

Clustering and Median Aggregation Improve Differentially Private Inference ```" for PT,

Reference 2

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d665ec5f-5103-41fe-abf5-7eb7d15fe2fa · outbound

This paper cites Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, and Robert Sim.

Clustering and Median Aggregation Improve Differentially Private Inference Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, and Robert Sim

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-07T10:47:28.698301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation b5209fe5-72e6-45a3-9096-cae457ce8279 · outbound

This paper cites Gecko: Versatile Text Embeddings Distilled from Large Language Models.

Clustering and Median Aggregation Improve Differentially Private Inference Gecko: Versatile Text Embeddings Distilled from Large Language Models

Reference 7

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unresolved
no resolver link, observed 2026-08-07T10:47:28.310943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:28.310943Z digest=sha256:4f11b128c072e8285706f082b1bead717bbc8c01fde291da37a3541f1f67a81d

Observation 770a8ee3-455f-4ade-82f0-f48599c77963 · outbound

This paper cites DPM: Clustering Sensitive Data through Separation.

Clustering and Median Aggregation Improve Differentially Private Inference DPM: Clustering Sensitive Data through Separation

Reference 8

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verified exact
local_arxiv, observed 2026-08-07T10:47:28.573265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:47:28.315143Z digest=sha256:2f4fac3a3d99b7f5d7e2eb34f0fef6d5a82c4b492b2dc78ceaac3821f1e3b1b9

Observation 06a5ffa1-3aea-4176-883a-e6f45b25c437 · outbound

This paper cites Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe.

Clustering and Median Aggregation Improve Differentially Private Inference Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe

Reference 12

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unresolved
no resolver link, observed 2026-08-07T10:47:28.332454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:28.332454Z digest=sha256:bcc25a104cbb41f2c6e70983a0f0d67050607aadb6929da79e1a032a9417b047

Observation 1f648d5f-fb8d-40d8-b5c3-cd92bbb57c07 · outbound

This paper cites Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, and Mrinmaya Sachan.

Clustering and Median Aggregation Improve Differentially Private Inference Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, and Mrinmaya Sachan

Reference 13

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unresolved
no resolver link, observed 2026-08-07T10:47:28.336748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 19d6df59-7df0-44b6-a24e-2be39ab58b76 · outbound

This paper cites doi: 10.18653/v1/2022.emnlp-main.323.

Clustering and Median Aggregation Improve Differentially Private Inference doi: 10.18653/v1/2022.emnlp-main.323

Reference 14

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no resolver link, observed 2026-08-07T10:47:28.340707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites Harnessing large-language models to generate private synthetic text.

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

Reference 15

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:28.344559Z digest=sha256:67531de81007a1c58c0027548f242d4a86dba4f508cff0a51ddbf37e9ac2514d

Observation 320db224-33c0-458a-b971-8f958dcdc07d · outbound

This paper cites KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server.

Clustering and Median Aggregation Improve Differentially Private Inference KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server

Reference 16

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verified exact
local_arxiv, observed 2026-08-07T10:47:28.516254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 8983b170-f5b0-43dc-be2a-dfa185831ead · outbound

This paper cites Differentially Private Tabular Data Synthesis using Large Language Models.

Clustering and Median Aggregation Improve Differentially Private Inference Differentially Private Tabular Data Synthesis using Large Language Models

Reference 17

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no resolver link, observed 2026-08-07T10:47:28.352313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:28.352313Z digest=sha256:6a1af2a6bb525f921361b00fd00e1b0efa10a0a5e7ea518b0a2b1f194c71f7df

Observation 1d21a1d2-aa29-442a-b6ca-999865c477bb · outbound

This paper cites Differentially private synthetic data via foundation model APIs 2: Text.

Clustering and Median Aggregation Improve Differentially Private Inference Differentially private synthetic data via foundation model APIs 2: Text

Reference 18

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verified fuzzy
raw_fallback, observed 2026-08-07T10:47:28.653406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:47:28.356116Z digest=sha256:a8fd1fba483c6f537837d15a2a66972e02e1aa1ecae751594c1aff72396c2c25

Observation 1353c80c-6876-48df-8833-a197b5089d4e · outbound

This paper cites Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model.

Clustering and Median Aggregation Improve Differentially Private Inference Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model

Reference 19

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unresolved
no resolver link, observed 2026-08-07T10:47:28.360138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a17bfe1a-76a2-4125-b201-350315fca8e5 · outbound

This paper cites Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning.

Clustering and Median Aggregation Improve Differentially Private Inference Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning

Reference 20

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unresolved
no resolver link, observed 2026-08-07T10:47:28.364112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:28.364112Z digest=sha256:69226632bea77f8ba95725631b2a8467a53531d0e4f631b40ec2cb7555b550d6

Observation 8a704e81-ed1e-4f6a-b3fc-de2c013d0472 · outbound

This paper cites Well-Read Students Learn Better: On the Importance of Pre-training Compact Models.

Clustering and Median Aggregation Improve Differentially Private Inference Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

Reference 23

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unresolved
no resolver link, observed 2026-08-07T10:47:28.375567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:28.375567Z digest=sha256:01ce9a45107249d22baca55e0ef0ae9289878b546e62c784eb1ee71d9ac073f9

Observation c2a2d73c-c41c-43e7-b582-7dca00520197 · outbound

This paper cites Data-dependent differentially private parameter learning for directed graphical models.

Clustering and Median Aggregation Improve Differentially Private Inference Data-dependent differentially private parameter learning for directed graphical models

Reference 2006

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verified fuzzy
raw_fallback, observed 2026-08-07T10:47:28.665051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:47:28.328409Z digest=sha256:87da3bc67db76442dbbf39c555c370741b36899ead1c6fa13752d5367937a066

Observation f07d82b3-df4a-408c-93c2-a33188c8a75c · outbound

This paper cites Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs.

Clustering and Median Aggregation Improve Differentially Private Inference Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs

Reference 2007

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verified exact
local_arxiv, observed 2026-08-07T10:47:28.556300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:47:28.319506Z digest=sha256:51eb92d34ef2b03e0fb678fb03e25f1dda7cfd75d0a3ee5429d273a0e236c3fd

Observation a74044ed-f187-4de3-b669-74407d328989 · outbound

This paper cites Nyt articles: 2.1m+ (2000-present),.

Clustering and Median Aggregation Improve Differentially Private Inference Nyt articles: 2.1m+ (2000-present),

Reference 2015

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raw_fallback, observed 2026-08-07T10:47:28.643382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:47:28.371964Z digest=sha256:0371d024267f48320ff1e9ca9b13d495cb3324d02a152b1df656c4c6dfa37be6

Observation 4b209dc9-e2a5-40f6-8198-303b35f6e068 · outbound

This paper cites Differentially private $k$-means clustering via exponential mechanism and max cover.

Clustering and Median Aggregation Improve Differentially Private Inference Differentially private $k$-means clustering via exponential mechanism and max cover

Reference 2017

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verified exact
local_arxiv, observed 2026-08-07T10:47:28.461917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:47:28.368125Z digest=sha256:fa7d52b5b3665dc2b3fb5fe868b42f6cefdda983edadbb737e1eb9d9cee07fbf

Observation 63d85b50-9541-4f51-978e-0a14bf1e9959 · outbound

This paper cites Prompt Public Large Language Models to Synthesize Data for Private On-device Applications.

Clustering and Median Aggregation Improve Differentially Private Inference Prompt Public Large Language Models to Synthesize Data for Private On-device Applications

Reference 2018

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verified exact
local_arxiv, observed 2026-08-07T10:47:28.612766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e8b150b9-94fe-4bb7-9819-39bf6ad81d2f · outbound

This paper cites Gecko: Versatile Text Embeddings Distilled from Large Language Models.

Clustering and Median Aggregation Improve Differentially Private Inference Gecko: Versatile Text Embeddings Distilled from Large Language Models

Reference 2021

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unresolved
no resolver link, observed 2026-08-07T10:47:28.306668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:28.306668Z digest=sha256:4fbfdc374e4dc2b3c643b9df4ebb1b55dee2358a0040b97266ee57fe836da791

Observation 0e5fbe64-7360-42f8-b302-f2b39bcd6b94 · outbound

This paper cites Differentially private decoding in large language models.

Clustering and Median Aggregation Improve Differentially Private Inference Differentially private decoding in large language models

Reference 2022

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verified fuzzy
raw_fallback, observed 2026-08-07T10:47:28.708180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation ecc44196-f5bf-4b13-8fcb-1f2473f13400 · outbound

This paper cites Adaptively Private Next-Token Prediction of Large Language Models.

Clustering and Median Aggregation Improve Differentially Private Inference Adaptively Private Next-Token Prediction of Large Language Models

Reference 2023

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unresolved
no resolver link, observed 2026-08-07T10:47:28.294391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:47:28.294391Z digest=sha256:843cbdf771dc93c37345bf524f2fe813b0e67f700cdf447392cdb4311ae67417

Observation 4704f9c5-5d7f-4384-8630-92472cc6cf04 · outbound

This paper cites Private prediction for large-scale synthetic text generation.

Clustering and Median Aggregation Improve Differentially Private Inference Private prediction for large-scale synthetic text generation

Reference 2024

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verified fuzzy
raw_fallback, observed 2026-08-07T10:47:28.687758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:47:28.302452Z digest=sha256:abff0ec94d201497a41f918bbd830404d6feaa7fc4def5079ec0458a98d6cc3f

Observation 001913cf-3c4d-41c0-a4f4-0e91d4566c39 · outbound

This paper cites Calibrating noise to sensitivity in private data analysis.

Clustering and Median Aggregation Improve Differentially Private Inference Calibrating noise to sensitivity in private data analysis

Reference 2025

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verified fuzzy
raw_fallback, observed 2026-08-07T10:47:28.676781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Pith citing papers

Observation 837152bf-e33f-42fe-8450-6f50255cdcc5 · inbound

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy cites this paper.

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy Clustering and Median Aggregation Improve Differentially Private Inference

Reference 52

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verified exact
arxiv_id, observed 2026-05-19T06:52:07.862154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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