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Paper Citation Record · LEDGER

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation

As of 7 August 2026, this Paper Citation Record lists 100 of 160 outbound references and 0 inbound Pith citation observations for arXiv:2506.12699.

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

pith.paper-citation-record.v1
2506.12699 v2

Coverage vector

measured 100 of 160 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:46:10.329817Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

100 of 160 outbound references displayed

  • verified exact6
  • verified fuzzy0
  • unresolved94
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8735cfe7-64ae-4124-9e6b-b31ef5905b16 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 1

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source=pdf_text observed=2026-08-07T00:46:09.878266Z digest=sha256:002ceb6ea0a72548bd472812a63883aefd034bb86a13d960208764271a13c29a

Observation 97f1ad6f-06ed-4038-9373-dda6f8f590f8 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-07T00:46:09.882756Z digest=sha256:cd3624b03f81a962bc4e95f0011f83fec89e7936479d9bd62cf73b287423b440

Observation ed641fd8-f759-4785-a020-0165e15301a5 · outbound

This paper cites Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien

Reference 3

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source=pdf_text observed=2026-08-07T00:46:09.886708Z digest=sha256:ecca5d6b585571f1f7d528e7722d7ec2c40c021e3c8d8be90b724784bee46c61

Observation e4a8f076-ba4e-45d7-90ee-90f9bfe1958c · outbound

This paper cites Adaptive PII Mitigation Framework for Large Language Models.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Adaptive PII Mitigation Framework for Large Language Models

Reference 4

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source=pdf_text observed=2026-08-07T00:46:09.890879Z digest=sha256:2c9eb076641b6a788d56559e63f50c3be341164999b63b8014e1a8d1d8488f12

Observation c9466f19-0998-4245-a98e-9eeb123531dc · outbound

This paper cites AirGapAgent: Protecting Privacy-Conscious Conversational Agents.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation AirGapAgent: Protecting Privacy-Conscious Conversational Agents

Reference 5

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source=pdf_text observed=2026-08-07T00:46:09.895706Z digest=sha256:b37399588b673b2989d2dbaecc0d8947ffad46ac7c66310c98dfbb184ec0f391

Observation 92c31aaa-8a7b-4a60-8743-dc4622023fc0 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Constitutional AI: Harmlessness from AI Feedback

Reference 7

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source=pdf_text observed=2026-08-07T00:46:09.905469Z digest=sha256:d6da2890f53ecae27bfc00cb89a58a15fb692293d80fecf65f9860fce954264d

Observation 4d9d7fde-3f5e-4d6e-af11-672f1ddcd2e2 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-07T00:46:09.909846Z digest=sha256:920da734ca87792b259eb29cd513c3c9393c6b59e5b3511518a8405cf47253de

Observation 9c0db24c-0e49-493e-96c5-222354fbac67 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 9

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source=pdf_text observed=2026-08-07T00:46:09.913974Z digest=sha256:8bbf29d18391f4694a055682cd86069812fd01bced36daadcb3868701e6c04e2

Observation e33709b9-3a99-46c5-b428-0d7c73201360 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 10

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source=pdf_text observed=2026-08-07T00:46:09.917695Z digest=sha256:70cb1cdc3a3890e9a0dd4a93f755d600b5eca5feb0a6c9f02cfaae48c3ae709b

Observation dc29043d-991c-447e-8cab-fc9bceb72dd4 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 11

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source=pdf_text observed=2026-08-07T00:46:09.921414Z digest=sha256:6e4477d21eaa09aae3244e7abaf575e2f41b2f3cc54782d8d3f1aa9747981a74

Observation 8ce678b5-8a24-433c-a68e-b2136c3e8165 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 12

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source=pdf_text observed=2026-08-07T00:46:09.925261Z digest=sha256:e631af493e80155197fa9499af6e215b9846a9aa3ecbd0a5f9b32bd26ead1bdb

Observation 64ba7733-0f44-4562-b418-08b9b2f7040a · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 13

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source=pdf_text observed=2026-08-07T00:46:09.929260Z digest=sha256:6456650fbd972573a1a0ab1af082ff357d6f305a3227ad1bf7b7ca3894553b2b

Observation b314572b-9dee-47e7-9e89-1dee93bb8c79 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-07T00:46:09.932970Z digest=sha256:731f3a532885f717b4299ea071796305c66eeec936ce52b05ccd76cc7a20a8b9

Observation 91abb483-006a-4da0-a612-a0d24d1a58da · outbound

This paper cites Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks

Reference 15

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source=pdf_text observed=2026-08-07T00:46:09.936979Z digest=sha256:a7cfb1bae92a14d73d4110bce7e9533c9ef0019d2570ba9307359992f627d772

Observation e7082d62-dd90-47f3-8eff-fc85e91221a6 · outbound

This paper cites THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption

Reference 16

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source=pdf_text observed=2026-08-07T00:46:09.940867Z digest=sha256:146aa4ea91a5234d1e6ad29ee7831260a8d003eef21845c3ac44c8b373292fde

Observation 645ef227-c9e9-4a15-ab9f-76f8cd4e53ff · outbound

This paper cites Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection

Reference 17

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source=pdf_text observed=2026-08-07T00:46:09.944887Z digest=sha256:71168301e62bc6e021b920539fcd10f2634300a2966b5d44dc117520c41ddadd

Observation b3362316-18bd-46ef-8ac5-9d47d725bebf · outbound

This paper cites Combating Adversarial Attacks with Multi-Agent Debate.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Combating Adversarial Attacks with Multi-Agent Debate

Reference 18

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source=pdf_text observed=2026-08-07T00:46:09.949867Z digest=sha256:af24002644134e67db320f53b827d41b916de1aa6c6227f96ffde78b589d83f5

Observation 16591195-72d2-4e77-ac91-d8511aac42d6 · outbound

This paper cites Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Models.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Models

Reference 19

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source=pdf_text observed=2026-08-07T00:46:09.954005Z digest=sha256:53d4e4c151c4b85db808bae29136b54c94074b3cda40b0f7b4f575e49bfef066

Observation 8deb338c-8eb3-4bc8-a220-41ca7b86d17c · outbound

This paper cites Security and Privacy Challenges of Large Language Models: A Survey.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Security and Privacy Challenges of Large Language Models: A Survey

Reference 20

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source=pdf_text observed=2026-08-07T00:46:09.958080Z digest=sha256:c8a7a2ff587325a7e6a6f33151fa89186e5eea7a0ce1c96f9847197eb5223e67

Observation 790ebb6d-4fad-4240-b81a-283de43f707d · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-07T00:46:09.962197Z digest=sha256:1b45895bd736f967bd18d70918af5d303d231cf5a9f8101dfcfb7c8f17ec8814

Observation 1975bde0-5dad-4dab-af52-f76b304b7856 · outbound

This paper cites TAG: Gradient Attack on Transformer-based Language Models.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation TAG: Gradient Attack on Transformer-based Language Models

Reference 22

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source=pdf_text observed=2026-08-07T00:46:09.966617Z digest=sha256:d5b0ddd0d3bc954ddbcf4418a34d1552daccb3bc3e6a39975d057d2b4a8c217b

Observation 8f514412-3aa2-4322-96af-dfdb8a54eddc · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-07T00:46:09.971434Z digest=sha256:0673efe4e305a87272ff60efe877ae966c84195b546890b126d74437d1c6482b

Observation 1d0d08d2-307c-4374-a5f5-b789cf0a5ff4 · outbound

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

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 24

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source=pdf_text observed=2026-08-07T00:46:09.976324Z digest=sha256:0dd3e89a09245635deef2788e294df9e8186f3a5053edd472a73a8de27a5c421

Observation bf17fd12-7aa9-4309-8cac-193f9a35e3e4 · outbound

This paper cites A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily

Reference 25

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source=pdf_text observed=2026-08-07T00:46:09.980899Z digest=sha256:6deec47d220e3104d9ea9706b974b2ef1f3e4a088fd385cc7b52c2b0d5ae7b82

Observation 5ccdb7a1-7a29-4bf2-a175-801fbf02547b · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-07T00:46:09.985886Z digest=sha256:4621d6634f753540800c1777d41067104283716ab86e4b598add0422f41e9ff6

Observation 277b88b9-20c0-49e4-b1eb-45e278e1b544 · outbound

This paper cites Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training

Reference 27

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source=pdf_text observed=2026-08-07T00:46:09.994790Z digest=sha256:5813f4754b47f94eb6c474639fb8035403510356749f7373feb12400087b6ae2

Observation a091b3c2-3e1f-4f11-ac4c-c4791576f909 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 28

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source=pdf_text observed=2026-08-07T00:46:09.999539Z digest=sha256:819ae82c147e14b7ae107a9e99c587408da6296095a98c3470c8577c21876ed5

Observation 94f448c2-11f7-440a-85be-8cfe9e22b7a6 · outbound

This paper cites Do Membership Inference Attacks Work on Large Language Models?.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Do Membership Inference Attacks Work on Large Language Models?

Reference 29

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Observation e7ff6528-8423-466e-a994-bdaa52ece841 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 30

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source=pdf_text observed=2026-08-07T00:46:10.012840Z digest=sha256:b0f155568aff1d0b8b713e73a4bc80d0d508749399837390c1656a27fbafa46d

Observation a247994c-b37f-4495-a224-64a4ea107083 · outbound

This paper cites Privacy Preserving Prompt Engineering: A Survey.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Privacy Preserving Prompt Engineering: A Survey

Reference 31

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source=pdf_text observed=2026-08-07T00:46:10.018951Z digest=sha256:a559470d9cedb5a738c861ce304db9c1223f5d78ea7c484f117596571cada863

Observation 2325caee-13cb-4a38-99b1-9f58f1dadf48 · outbound

This paper cites Ekenstam.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Ekenstam

Reference 32

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raw_fallback, observed 2026-08-07T00:46:11.985987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a625eacf-d782-4124-95f3-457d74ccc836 · outbound

This paper cites Who's Harry Potter? Approximate Unlearning in LLMs.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Who's Harry Potter? Approximate Unlearning in LLMs

Reference 33

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Observation d1c70ac8-1979-448a-8e2a-216eac2e053b · outbound

This paper cites FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 34

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Observation cc24ff99-3765-4f12-9322-603b44b23e2f · outbound

This paper cites LLM Agents can Autonomously Exploit One-day Vulnerabilities.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation LLM Agents can Autonomously Exploit One-day Vulnerabilities

Reference 35

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source=pdf_text observed=2026-08-07T00:46:10.038821Z digest=sha256:5f8a56562e37362fbd3cb71b84c4acb65c98786c10f9c1540a037ad1323505b8

Observation 2fac7435-6924-4d78-954e-cae7a218f97e · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 36

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source=pdf_text observed=2026-08-07T00:46:10.043264Z digest=sha256:81d25f7f64496d7ab2a72588fd4767085888ebca103897eb4adf8f144bbdb895

Observation 2e01204b-f9ef-4045-94cc-20834627c045 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 37

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source=pdf_text observed=2026-08-07T00:46:10.047102Z digest=sha256:f450a602709dbc979abe99e0670977d6541cf2e9cd2ac1ab11d0ddbed417faae

Observation f329733a-b640-47c5-8354-6fe710c66177 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 38

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source=pdf_text observed=2026-08-07T00:46:10.056737Z digest=sha256:5ca06e53dc7e0e7959333fce435690aefbab0e59e6b6790e20a68749d336d5ae

Observation a689e14e-1ca4-4d1d-aac2-5a708c41120a · outbound

This paper cites Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast

Reference 39

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Observation 5b415b0a-5d0f-4d46-8bc5-110356366d79 · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 40

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Observation f5e745d2-5c9a-4234-8dbc-cebb5b63870a · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 41

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source=pdf_text observed=2026-08-07T00:46:10.070506Z digest=sha256:69a0faf15f0e4f949622a8155c8da8f224520a0656e5a1b9dacd5a7cc9a15782

Observation 344cfbea-8d86-4ea1-8ec7-82c8d0c3605e · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 42

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source=pdf_text observed=2026-08-07T00:46:10.074672Z digest=sha256:f938ef04d685c28d3c9439373f56b9ded4b3e57ae216246075a8d9c05d935a2c

Observation 2672f80e-e0f6-4fb6-875e-1c7caee3d9c7 · outbound

This paper cites Can LLMs get help from other LLMs without revealing private information?.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Can LLMs get help from other LLMs without revealing private information?

Reference 43

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source=pdf_text observed=2026-08-07T00:46:10.078568Z digest=sha256:fcff86f258861451c53ef55235cef8d653457ad3697f41fdb44a6b444205bad9

Observation 23eb3263-f1bc-4116-a9a2-6e8295eba6dd · outbound

This paper cites TrustAgent: Towards Safe and Trustworthy LLM-based Agents.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation TrustAgent: Towards Safe and Trustworthy LLM-based Agents

Reference 44

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source=pdf_text observed=2026-08-07T00:46:10.083811Z digest=sha256:2edba56a40fe783ff3f9a81a9c3c3a8146cf7eff447c4b8d0db94ab8e0342899

Observation 71830973-cea3-44c7-8ed1-89f95e9cf01e · outbound

This paper cites Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy

Reference 45

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source=pdf_text observed=2026-08-07T00:46:10.088056Z digest=sha256:b238c0197047f7db2dc1687bb5ebb2ad0f5c28e1186e30b0f21ae31f746cfe61

Observation 3471d20b-b584-48ff-99ca-ce2e80c38b12 · outbound

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

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Membership Inference Attack Susceptibility of Clinical Language Models

Reference 46

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source=pdf_text observed=2026-08-07T00:46:10.092761Z digest=sha256:d3feee85229fba58dad55cbbaee08e973becfc2cb13038ce84c2df4bec0aa60f

Observation ce438300-d9a4-4d31-b98a-0eb2f309df2c · outbound

This paper cites Measuring Forgetting of Memorized Training Examples.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Measuring Forgetting of Memorized Training Examples

Reference 47

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source=pdf_text observed=2026-08-07T00:46:10.097075Z digest=sha256:b7df980f236e2d019759ad5f375d029a3606568216cbd6f80132e1bf0fed5607

Observation c052bfc3-9b9d-4008-b4da-9f4b4c9012c3 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 48

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source=pdf_text observed=2026-08-07T00:46:10.101255Z digest=sha256:973540f307b886bc7c135ebab40cec32495374dffc34ecc78eb6e8af65ae1f42

Observation 7b073924-3383-4525-b086-bc29fb75dc37 · outbound

This paper cites Navigating LLM Ethics: Advancements, Challenges, and Future Directions.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Navigating LLM Ethics: Advancements, Challenges, and Future Directions

Reference 49

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source=pdf_text observed=2026-08-07T00:46:10.104827Z digest=sha256:f430ecb3e6ea593e4eb4173e02e163135e2d21289fea741c2e9ed5a6e9cbe7fe

Observation c5bb4366-601c-4bd6-806a-1f8d6329eff0 · outbound

This paper cites User Inference Attacks on Large Language Models.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation User Inference Attacks on Large Language Models

Reference 50

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source=pdf_text observed=2026-08-07T00:46:10.108829Z digest=sha256:7808ba139d980a02c0baa727df2d06b0ed04daa8f5ac50405da70e8f8d8a261b

Observation 359b03c0-af9e-4f4e-bbd4-34ba76b3a984 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 51

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source=pdf_text observed=2026-08-07T00:46:10.112774Z digest=sha256:ba4570caeb708520e3ef7178b8765ff849eae2a37a5463788c8e22ffda6ce449

Observation 0bbaf945-3fbb-412b-888d-12b6e30a0c10 · outbound

This paper cites Copyright Violations and Large Language Models.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Copyright Violations and Large Language Models

Reference 52

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source=pdf_text observed=2026-08-07T00:46:10.117323Z digest=sha256:af13355cbabbd676d0423946eb2d8244f546500c2d03f68d744ee443db718c50

Observation 01ff4e5f-a0c9-45fd-bf85-1e45f6c36d16 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 53

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source=pdf_text observed=2026-08-07T00:46:10.121598Z digest=sha256:67bd3ae9589e0fe4e5cb08a627e2c1b03f343a8c2d5c399b57f0ff67969f040f

Observation 9fb467b9-f255-4526-bb6b-6127d1da313a · outbound

This paper cites On the Effectiveness of Regularization Against Membership Inference Attacks.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation On the Effectiveness of Regularization Against Membership Inference Attacks

Reference 54

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source=pdf_text observed=2026-08-07T00:46:10.125502Z digest=sha256:aa3c5d612082af0c5ae441c1144257f1d28ee17949f941fc32c1aefc63e4ad46

Observation a97f31a1-4def-4b89-8899-916d2862bedc · outbound

This paper cites ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review

Reference 55

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source=pdf_text observed=2026-08-07T00:46:10.129541Z digest=sha256:9441a7b7a321fb790004d9be0c28a5f4f13e004ff6d1b64ddcac71061d09d2a3

Observation 247b37b0-886d-4a4c-9a03-60a0af906558 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 56

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source=pdf_text observed=2026-08-07T00:46:10.133665Z digest=sha256:4cfb9c2d9e5aa49893a667550584d69b5fbd67158af74a2cae98fda5cc9d63ae

Observation acc0b6f0-f2ec-44c1-888b-7e959ee06419 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 57

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source=pdf_text observed=2026-08-07T00:46:10.137660Z digest=sha256:e6016879ae231c9e22d544d92a67e1161efde5ecbd22c5d3b9a0cde1938e551f

Observation e2e5fb40-0910-41f1-bbb7-89f039cefb58 · outbound

This paper cites FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

Reference 58

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source=pdf_text observed=2026-08-07T00:46:10.141934Z digest=sha256:fd62a9f09b21500aff10c33257134adfad6ee5239fe8d520f216e6d8038884e5

Observation 31ab7497-1980-49ab-92c8-af798d34ed55 · outbound

This paper cites RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 59

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source=pdf_text observed=2026-08-07T00:46:10.146710Z digest=sha256:d930b1244f4f728e230512959adf2a4e212151d2a1591853a6d4dbb180dbb7e8

Observation 86ba1150-60aa-4fa2-a57d-ae0a840b7a78 · outbound

This paper cites Deduplicating Training Data Makes Language Models Better.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Deduplicating Training Data Makes Language Models Better

Reference 60

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source=pdf_text observed=2026-08-07T00:46:10.150709Z digest=sha256:9baff6a3b50a1cfe57d905ae8407c9367074d7700a943abf613fc7208ee77833

Observation a3ee3a2d-0e4c-4f17-91d0-75218a3be1a9 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 61

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source=pdf_text observed=2026-08-07T00:46:10.155582Z digest=sha256:e0008fe79dfaa084aa41faee8b759a5686402011d604aa2ba3aff4ad5c239c17

Observation 8ee003a4-d249-40a4-8d2b-ffcf7a084972 · outbound

This paper cites Multi-step Jailbreaking Privacy Attacks on ChatGPT.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Multi-step Jailbreaking Privacy Attacks on ChatGPT

Reference 62

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source=pdf_text observed=2026-08-07T00:46:10.159449Z digest=sha256:b0d6c3cf8018490b87ef58849dc61f6cff699ee97d186412407c418a1975685f

Observation acafedcd-9cae-4723-bedd-5d0ee111464a · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 63

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source=pdf_text observed=2026-08-07T00:46:10.164065Z digest=sha256:632a6780ef84025bec59dd3ad83ce8d55147938834ef5a626e44730fc53744d5

Observation 32b8f661-b285-44c1-a1f0-328f22ee1280 · outbound

This paper cites Text Adversarial Purification as Defense against Adversarial Attacks.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Text Adversarial Purification as Defense against Adversarial Attacks

Reference 64

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source=pdf_text observed=2026-08-07T00:46:10.168237Z digest=sha256:8129c053bb96025cb8fc6e20e2bc2eb68b4828f32c89b9269e84a52e03c633cc

Observation 6c4fdef9-f070-4ea8-b739-1f8c21699090 · outbound

This paper cites Human-Centered Privacy Research in the Age of Large Language Models.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Human-Centered Privacy Research in the Age of Large Language Models

Reference 65

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source=pdf_text observed=2026-08-07T00:46:10.172818Z digest=sha256:f55011e095be5a26c327b65d708c6e7372082318218af34dc7ec3082d059db81

Observation 8013d41a-81b1-42a4-bcd2-a99e88ecf4d1 · outbound

This paper cites Large Language Models Can Be Strong Differentially Private Learners.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Large Language Models Can Be Strong Differentially Private Learners

Reference 66

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source=pdf_text observed=2026-08-07T00:46:10.178309Z digest=sha256:5172d1716a578b21a523f2ab01551b0adb18853a0b76fdefc3aa9810e67a96fc

Observation a7654b3e-b578-4150-8e2d-a553b2d2a7b5 · outbound

This paper cites Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

Reference 67

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source=pdf_text observed=2026-08-07T00:46:10.182617Z digest=sha256:b83ca1571dbd6ec78bfdd5c7a477bcfed42bec4a534d07b90ddc4027ed78d010

Observation 1410d7d2-f156-4ff8-9196-5e92fe386090 · outbound

This paper cites EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs

Reference 68

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source=pdf_text observed=2026-08-07T00:46:10.186603Z digest=sha256:9f19b92302e89256b4301d69ed731e5eb4bbfab66fd8e6045344694472bdc829

Observation 8641b70a-73f5-4610-a1ac-0a8bc454c018 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 69

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source=pdf_text observed=2026-08-07T00:46:10.192023Z digest=sha256:5a7ea6617205bc7eea0236a9ddf1fb87251a6f4f547c58a34af4575ae28a9a3d

Observation 0bb72fd1-f74b-478f-929d-341da8fa7af3 · outbound

This paper cites Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study

Reference 70

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source=pdf_text observed=2026-08-07T00:46:10.201081Z digest=sha256:27a488f0f3aa91e85ff9db47d654a3b3c165effe7370c71f582a648b621f2c49

Observation e1d4d83b-176d-40f0-bc97-3eaa9acbc41c · outbound

This paper cites MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Reference 71

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source=pdf_text observed=2026-08-07T00:46:10.205737Z digest=sha256:1b138aa6291d180262bc6c584541142bf0a0fd48938413845d6726e0a0deeea0

Observation 8978e9ff-5f35-413f-ab36-4d4ce4d500a3 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 72

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source=pdf_text observed=2026-08-07T00:46:10.210424Z digest=sha256:c804605c1ced17eb5f7cba856d1fa74e64706eb5425c9d61581073b097646fe2

Observation 6c4d199a-318c-48a2-ac4b-8c03d7244387 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 73

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source=pdf_text observed=2026-08-07T00:46:10.214450Z digest=sha256:b659b9cd029683aab512bf50810bde354509a8d75fcea5ed2f3e7b13c6a21e57

Observation 60fe569a-c628-4c1b-8025-7d9c25363c47 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 74

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source=pdf_text observed=2026-08-07T00:46:10.218245Z digest=sha256:f261f8a03b7fc138c279b9b96d0092d567b1f81ee561f12482c463845d6bf80a

Observation a493dc94-f6f9-45e4-8c84-4424dd39b06f · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 75

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source=pdf_text observed=2026-08-07T00:46:10.222754Z digest=sha256:71916b9c30f72c6df7b7b756c2475fccd1a039c65aa0e7b4645194a78de029bc

Observation a394d109-ef5c-4b68-a2b1-739b6f3181b4 · outbound

This paper cites Secret Collusion among AI Agents: Multi-Agent Deception via Steganography.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Secret Collusion among AI Agents: Multi-Agent Deception via Steganography

Reference 76

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source=pdf_text observed=2026-08-07T00:46:10.227961Z digest=sha256:2c186cf70d3ac19b426fa368f461bce60de557d960409aaebb14c6c7305eb62b

Observation 017bb9fe-4dc4-4907-b71d-a138c567956d · outbound

This paper cites Testing Language Model Agents Safely in the Wild.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Testing Language Model Agents Safely in the Wild

Reference 77

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source=pdf_text observed=2026-08-07T00:46:10.232093Z digest=sha256:f0536de7385db39e94f4abac20a937c07808a28fbbcceea2a761100ea335693e

Observation 7c9ee1e7-6215-405f-a051-886c5e951a5d · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation WebGPT: Browser-assisted question-answering with human feedback

Reference 78

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source=pdf_text observed=2026-08-07T00:46:10.236136Z digest=sha256:ad03da892ec83ac601156a596fe4b106e4560a34d833383923a818dbbfb796cf

Observation 4ee2237d-86bb-4c83-956a-2719877f4c06 · outbound

This paper cites Scalable Extraction of Training Data from (Production) Language Models.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Scalable Extraction of Training Data from (Production) Language Models

Reference 79

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source=pdf_text observed=2026-08-07T00:46:10.240817Z digest=sha256:077e9161dc56e63d95a735282dd15fcf184aa3c96d6a1817dfabbbb3cec5af77

Observation f0c379e6-3b3d-40ec-a69f-de096a767e22 · outbound

This paper cites Privacy Issues in Large Language Models: A Survey.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Privacy Issues in Large Language Models: A Survey

Reference 80

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no resolver link, observed 2026-08-07T00:46:10.244584Z

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source=pdf_text observed=2026-08-07T00:46:10.244584Z digest=sha256:698824c949c0fb5e693931691f9fb2dfce4b0de77e6eda6aa339e68af5d67118

Observation fc4fff79-bed4-4235-81bd-f181eadbcb5e · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 81

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source=pdf_text observed=2026-08-07T00:46:10.248312Z digest=sha256:3962547e012e7e480e63307f603b29105f29b8ecde3a42e04b4367a20c80d479

Observation 163e0db5-a564-437b-ae9d-c92bdf8a90d1 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 82

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source=pdf_text observed=2026-08-07T00:46:10.251921Z digest=sha256:1d89574b0effc8ef84d0e7b8b5511404c36a8549f5985cd5dd70cd51cd3dc9e3

Observation 0e549ca7-cca5-48c6-85c5-477be4655943 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 83

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source=pdf_text observed=2026-08-07T00:46:10.256229Z digest=sha256:09124aff42a5e0ab5728b80a32c19423158190f20856db6823b9e05cc142ae9b

Observation 2e7469bc-7198-4777-b1b2-bce72bb4f9f5 · outbound

This paper cites Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation

Reference 84

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source=pdf_text observed=2026-08-07T00:46:10.260774Z digest=sha256:f1ca4211cd17272f92c67233e9f705636a08273853fe259ca380417b9a9c27cc

Observation 0303c728-8883-4864-9225-339e3630f5ab · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 85

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source=pdf_text observed=2026-08-07T00:46:10.264840Z digest=sha256:0d72f6a926e10f25ed1191c7eeea8ec892c19d599f00bde63bfd691663bcceaf

Observation 60e94ea9-6060-4362-a0a3-d66b67897787 · outbound

This paper cites Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization

Reference 86

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source=pdf_text observed=2026-08-07T00:46:10.268928Z digest=sha256:a81d8d624814fc29d649f0fae42a5ab46b0acc435634e6772e28a212da80bd86

Observation 927d5938-730c-4533-b171-657972d434e5 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 87

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source=pdf_text observed=2026-08-07T00:46:10.272761Z digest=sha256:26006962f328b150774cf9adbbcb772917d97eee3ce8400a961afa0d71198e8a

Observation 53501d80-3eda-4740-bb0c-5dd8c415a90c · outbound

This paper cites Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks

Reference 88

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source=pdf_text observed=2026-08-07T00:46:10.276577Z digest=sha256:2563cbd74145c6a6444eea89d6db0c5fc9906d4cb86a0f4d073e29ef28ee6485

Observation bb06e0d8-eefc-478b-8e23-50ed228698a8 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 89

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source=pdf_text observed=2026-08-07T00:46:10.280592Z digest=sha256:0636f377a49d612514a63d5da452cecfdff0d1e0393c50a8121c80701c7ee398

Observation 76cd9b02-c281-4e6e-ad6a-3aa547aa7b5e · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 90

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source=pdf_text observed=2026-08-07T00:46:10.284843Z digest=sha256:df18f552c85d0fac93c7b27376131691b4467b5768d24be84a175b285e69ed61

Observation 6880f691-d74b-4467-8de1-68a6884905d1 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 91

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source=pdf_text observed=2026-08-07T00:46:10.288651Z digest=sha256:051d7712e4f18b619ed5621d47452b47e092918cc0db24a77394375a0a2bf39b

Observation 2a224452-8a38-4d6c-b6c7-37353f505476 · outbound

This paper cites Encryption-Friendly LLM Architecture.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Encryption-Friendly LLM Architecture

Reference 92

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source=pdf_text observed=2026-08-07T00:46:10.292901Z digest=sha256:73af26f4640f64562dbee0c7b447aecb8def45bdfe2ba3f24a7e17b63726bcd1

Observation 08925f9b-1a7a-473e-9014-f3f1a03a45e0 · outbound

This paper cites Why should i trust you?.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Why should i trust you?

Reference 93

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source=pdf_text observed=2026-08-07T00:46:10.297534Z digest=sha256:5cda9c05ba2af774e598017f349e7c3396ac150dd49d15a0a432383fadc2c937

Observation e09991f1-1dfc-4010-9c00-cafb84c15078 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 94

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source=pdf_text observed=2026-08-07T00:46:10.301358Z digest=sha256:f2641fa8cd00df652ff913b1b3117c6b7be6dc88c2fbaf4578cb42c1803dc011

Observation 41daa235-5a1b-45cd-8916-3802127887f3 · outbound

This paper cites Efficient Language Model Architectures for Differentially Private Federated Learning.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Efficient Language Model Architectures for Differentially Private Federated Learning

Reference 95

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verified exact
local_arxiv, observed 2026-08-07T00:46:11.183127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:46:10.305019Z digest=sha256:3289dcd48a112d208ef102b6555d081158a86b6738f8d46e842ca4a89d189024

Observation 96176b6e-3c80-4a61-91da-fac799253e24 · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 96

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source=pdf_text observed=2026-08-07T00:46:10.309375Z digest=sha256:458a1be1a8d5c5f8fed23b00f5fddbadf106d7230cea8a885d11355ea29d83f0

Observation 5b0574ca-86df-4f12-aebe-90ea31d23274 · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 97

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source=pdf_text observed=2026-08-07T00:46:10.313298Z digest=sha256:2ce59b4629b16040818ce52e71343e8d0658e860cf079b4384dcb4c9a5496a9b

Observation 127a966c-bc96-43aa-9665-d323b3f154df · outbound

This paper cites an unresolved cited work.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Unresolved cited work

Reference 98

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no resolver link, observed 2026-08-07T00:46:10.317499Z

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source=pdf_text observed=2026-08-07T00:46:10.317499Z digest=sha256:46b6a685c5f8ebd8efb0d5a0985a1d1fed169964aadbdc7af61747a603d508e4

Observation a791b14b-7418-45a6-8c58-e0a7c4a5349a · outbound

This paper cites Identifying the Risks of LM Agents with an LM-Emulated Sandbox.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Identifying the Risks of LM Agents with an LM-Emulated Sandbox

Reference 99

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source=pdf_text observed=2026-08-07T00:46:10.321672Z digest=sha256:e6fe7cbe617b9e9535e364fe64f264b3a5f821d029628836b7f5d5bd401735df

Observation af657e7c-3178-47ce-89b3-c961e5cc741b · outbound

This paper cites Practical Secure Inference Algorithm for Fine-tuned Large Language Model Based on Fully Homomorphic Encryption.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Practical Secure Inference Algorithm for Fine-tuned Large Language Model Based on Fully Homomorphic Encryption

Reference 100

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:46:11.130238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:46:10.325905Z digest=sha256:053331ac19aeab2faf6661a32e41ff7ab623d78d8b43780bda832e4476e088f2

Observation e99fef94-2a25-4944-8583-5bc2ceab55cf · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 101

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T00:46:10.329817Z digest=sha256:0f084849db5064361ab7c7c4a489cca2e956465e3264d55c83bc44595cfce875

Pith citing papers

No inbound Pith citation observations are available.