Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:04:41.540024Z
Paper Citation Record · LEDGER
Quantifying Mix Network Privacy Erosion with Generative Models
As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2506.08918.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
52 of 52 outbound references displayed
- verified exact1
- verified fuzzy40
- unresolved11
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation fe7d8cee-1917-4b86-9d6c-5c5ef2373c16 · outbound
This paper cites MCMix: Anonymous Messaging via Secure Multiparty Computation.
Quantifying Mix Network Privacy Erosion with Generative Models MCMix: Anonymous Messaging via Secure Multiparty Computation
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.103194Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ee3d8fae-a2e8-429e-9d4b-9163f87e0b35 · outbound
This paper cites Mixflow: Assessing mixnets anonymity with contrastive architectures and semantic network information.IACR Cryptol.
Quantifying Mix Network Privacy Erosion with Generative Models Mixflow: Assessing mixnets anonymity with contrastive architectures and semantic network information.IACR Cryptol
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.093061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c6830c5d-2990-4a43-a3a2-01928b9a91f9 · outbound
This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.
Quantifying Mix Network Privacy Erosion with Generative Models Neural Machine Translation by Jointly Learning to Align and Translate
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.082304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b1897bb8-9f37-4072-aef4-0e0e8feaf10c · outbound
This paper cites Longformer: The Long-Document Transformer.
Quantifying Mix Network Privacy Erosion with Generative Models Longformer: The Long-Document Transformer
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.392653Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 218fd660-ae98-42ab-b30a-b1e9cf8c6ef5 · outbound
This paper cites Var-cnn: A data-efficient website fingerprinting attack based on deep learning.Proceedings on Privacy Enhancing Technologies, 2019(4):292–310, 2019.
Quantifying Mix Network Privacy Erosion with Generative Models Var-cnn: A data-efficient website fingerprinting attack based on deep learning.Proceedings on Privacy Enhancing Technologies, 2019(4):292–310, 2019
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.071636Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f3b3fb87-ee19-476e-b5c3-c782cdc3173e · outbound
This paper cites Language Models are Few-Shot Learners.
Quantifying Mix Network Privacy Erosion with Generative Models Language Models are Few-Shot Learners
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.060869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c166afc3-a47a-40f6-9492-73cea8d3218f · outbound
This paper cites an unresolved cited work.
Quantifying Mix Network Privacy Erosion with Generative Models Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T05:04:42.049731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5677f0be-c128-4a0f-b23f-9449a41ce5dc · outbound
This paper cites Untraceable electronic mail, return addresses, and digital pseudonyms.
Quantifying Mix Network Privacy Erosion with Generative Models Untraceable electronic mail, return addresses, and digital pseudonyms
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.038945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0b78b1bb-6d7a-4080-95c1-64603cc39eb9 · outbound
This paper cites Asoni, Barrera, OSC, David, George Danezis, and Adrain Perrig.
Quantifying Mix Network Privacy Erosion with Generative Models Asoni, Barrera, OSC, David, George Danezis, and Adrain Perrig
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.028275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d18e23e0-dc20-4feb-abf6-ee3324b36923 · outbound
This paper cites Thetrafficanalysisofcontinuous-timemixes.
Quantifying Mix Network Privacy Erosion with Generative Models Thetrafficanalysisofcontinuous-timemixes
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.017537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1fdd57f2-37c4-4619-8b66-d66193d7ea61 · outbound
This paper cites Traffic Analysis of the HTTP Protocol over TLS, 2009.
Quantifying Mix Network Privacy Erosion with Generative Models Traffic Analysis of the HTTP Protocol over TLS, 2009
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:42.006829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6154ca0e-2a1b-4445-a995-5cad83edf600 · outbound
This paper cites Sphinx: A compact and provably secure mix format.
Quantifying Mix Network Privacy Erosion with Generative Models Sphinx: A compact and provably secure mix format
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.996366Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7b7a318f-2523-4c07-95aa-5510f9ff7a83 · outbound
This paper cites Flashattention: Fast andmemory-efficientexactattentionwithio-awareness.Advances in Neural Information Processing Systems, 35:16344–16359, 2022.
Quantifying Mix Network Privacy Erosion with Generative Models Flashattention: Fast andmemory-efficientexactattentionwithio-awareness.Advances in Neural Information Processing Systems, 35:16344–16359, 2022
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.985952Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation bc6f0f17-d60c-419d-a42e-668b6baa6368 · outbound
This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
Quantifying Mix Network Privacy Erosion with Generative Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.426193Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b66b2715-5c60-494f-908e-d6231867cb2a · outbound
This paper cites The Nym Network: The Next Generation of Privacy Infrastructure.White Paper, Version 1.0, 2021.
Quantifying Mix Network Privacy Erosion with Generative Models The Nym Network: The Next Generation of Privacy Infrastructure.White Paper, Version 1.0, 2021
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.975086Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 29d63335-0b14-4247-a906-aa871d016831 · outbound
This paper cites Taxonomy of Mixes and Dummy Traffic.
Quantifying Mix Network Privacy Erosion with Generative Models Taxonomy of Mixes and Dummy Traffic
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.964902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5c8b21a9-03d6-4293-b5f7-3805aff167b7 · outbound
This paper cites Comparison Between Two Practical Mix Designs.
Quantifying Mix Network Privacy Erosion with Generative Models Comparison Between Two Practical Mix Designs
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.956047Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 4b64042b-b0d1-4f1b-9e04-f460cd6eceb5 · outbound
This paper cites Anonymity loves company: Usability and the network effect.
Quantifying Mix Network Privacy Erosion with Generative Models Anonymity loves company: Usability and the network effect
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.947338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5db0d338-4bdd-4f1a-8ad8-9632e42d2052 · outbound
This paper cites I know what you saw last minute—encrypted http adaptive video streaming title classification.IEEE trans- actions on information forensics and security, 12(12):3039–3049, 2017.
Quantifying Mix Network Privacy Erosion with Generative Models I know what you saw last minute—encrypted http adaptive video streaming title classification.IEEE trans- actions on information forensics and security, 12(12):3039–3049, 2017
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.937826Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c5947d58-9048-4591-930c-f415afdafaef · outbound
This paper cites The Norwegian Internet Voting Protocol.
Quantifying Mix Network Privacy Erosion with Generative Models The Norwegian Internet Voting Protocol
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.928176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 97d81c8d-81c3-4748-a4d6-aa161fe3c09d · outbound
This paper cites Mixnet optimization methods.Proceedings on Privacy Enhancing Technologies, 1:22, 2022.
Quantifying Mix Network Privacy Erosion with Generative Models Mixnet optimization methods.Proceedings on Privacy Enhancing Technologies, 1:22, 2022
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.917874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1a37cbb5-5973-459d-921e-b043b4bb3b50 · outbound
This paper cites k-fingerprinting: A robust scalable website finger- printing technique.
Quantifying Mix Network Privacy Erosion with Generative Models k-fingerprinting: A robust scalable website finger- printing technique
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.906798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 3609acaf-e164-4427-beeb-312155259923 · outbound
This paper cites https://hoprnet.org/Book_Of_Hopr_2021.01_v1.
Quantifying Mix Network Privacy Erosion with Generative Models https://hoprnet.org/Book_Of_Hopr_2021.01_v1
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.896064Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 82111c2c-88cc-4ea4-a65a-c9f91f2947c3 · outbound
This paper cites On Privacy Notions in Anonymous Communication.Proceedings on Privacy Enhancing Technologies, 2019.
Quantifying Mix Network Privacy Erosion with Generative Models On Privacy Notions in Anonymous Communication.Proceedings on Privacy Enhancing Technologies, 2019
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.885829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1eaff575-24fd-4403-84f7-96b4fa992bc6 · outbound
This paper cites Karaoke:Distributedprivatemessaging immune to passive traffic analysis.
Quantifying Mix Network Privacy Erosion with Generative Models Karaoke:Distributedprivatemessaging immune to passive traffic analysis
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.876306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2942f263-71b2-4a4c-bebc-2bad6c5da845 · outbound
This paper cites Stopping Silent Sneaks: Defending against Malicious Mixes with Topological Engineering.
Quantifying Mix Network Privacy Erosion with Generative Models Stopping Silent Sneaks: Defending against Malicious Mixes with Topological Engineering
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T05:04:41.624735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 43d59b5b-fe75-42ec-a2cd-951168346677 · outbound
This paper cites An Empirical Model of Large-Batch Training.
Quantifying Mix Network Privacy Erosion with Generative Models An Empirical Model of Large-Batch Training
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.465951Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d18f4538-aa93-4eb2-b4ef-44fc2642a442 · outbound
This paper cites Distributed Representations of Words and Phrases and their Compositionality.
Quantifying Mix Network Privacy Erosion with Generative Models Distributed Representations of Words and Phrases and their Compositionality
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.865674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0f610e51-c1b2-4c4c-a284-31e47915c09f · outbound
This paper cites I know why you went to the clinic: Risks and realization of https traffic analysis.
Quantifying Mix Network Privacy Erosion with Generative Models I know why you went to the clinic: Risks and realization of https traffic analysis
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.856165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2593f8d8-e1e1-45b6-bb46-450a0cf82af8 · outbound
This paper cites GPT-4 Technical Report.
Quantifying Mix Network Privacy Erosion with Generative Models GPT-4 Technical Report
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.475041Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 142dfa04-6520-483c-8404-ba7819c17aeb · outbound
This paper cites Do dummies pay off? limits of dummy traffic protection in anonymous communications.
Quantifying Mix Network Privacy Erosion with Generative Models Do dummies pay off? limits of dummy traffic protection in anonymous communications
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.846482Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e70a781a-7273-45af-86a3-a3ac56f1f3e1 · outbound
This paper cites Understanding the effects of real-world behavior in statistical disclosure attacks.
Quantifying Mix Network Privacy Erosion with Generative Models Understanding the effects of real-world behavior in statistical disclosure attacks
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.836719Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2032260d-a8f8-482c-a36e-06480af08419 · outbound
This paper cites Meet the family of statisticaldisclosureattacks.
Quantifying Mix Network Privacy Erosion with Generative Models Meet the family of statisticaldisclosureattacks
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.826464Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b2932c39-29e5-434a-ae78-23ede8188c2b · outbound
This paper cites Website fingerprinting at internet scale.
Quantifying Mix Network Privacy Erosion with Generative Models Website fingerprinting at internet scale
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.486288Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd00c7b2-a174-4286-9be3-9ed656581613 · outbound
This paper cites GloVe: Global Vectors for Word Representation.
Quantifying Mix Network Privacy Erosion with Generative Models GloVe: Global Vectors for Word Representation
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.809522Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8acd3268-cffd-4e31-88ae-fa0cac799ce1 · outbound
This paper cites Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer.
Quantifying Mix Network Privacy Erosion with Generative Models Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.800008Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 568790c7-f45b-41e2-8556-e52d1492b43a · outbound
This paper cites Piotrowska.
Quantifying Mix Network Privacy Erosion with Generative Models Piotrowska
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.790248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation cfcd7469-4fae-4755-890f-c98e65862b36 · outbound
This paper cites Piotrowska.
Quantifying Mix Network Privacy Erosion with Generative Models Piotrowska
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.780470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6671bb5b-b168-4d13-93a9-0b0910c95b92 · outbound
This paper cites The loopix anonymity system.
Quantifying Mix Network Privacy Erosion with Generative Models The loopix anonymity system
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.770868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6545259b-2276-47c9-8f39-7fb8bec4d2db · outbound
This paper cites an unresolved cited work.
Quantifying Mix Network Privacy Erosion with Generative Models Unresolved cited work
Reference 40
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T05:04:41.760535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9c16a226-24ee-4a82-94ae-19ece53a663a · outbound
This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.
Quantifying Mix Network Privacy Erosion with Generative Models Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.506194Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18145bf5-c759-45bf-b178-a165920a20e1 · outbound
This paper cites Towards an Information Theoretic Metric for Anonymity.
Quantifying Mix Network Privacy Erosion with Generative Models Towards an Information Theoretic Metric for Anonymity
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.749823Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 132d1c88-0317-4142-b00c-872a7725ab35 · outbound
This paper cites From a Trickle to a Flood: Active Attacks on Several Mix Types.
Quantifying Mix Network Privacy Erosion with Generative Models From a Trickle to a Flood: Active Attacks on Several Mix Types
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.740541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8ed9fb36-3e97-4aca-808b-8087395132ff · outbound
This paper cites Deep fingerprinting: Undermining website fingerprinting defenses with deep learning.
Quantifying Mix Network Privacy Erosion with Generative Models Deep fingerprinting: Undermining website fingerprinting defenses with deep learning
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.730158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 86ec900b-5984-4196-8fad-ab393b74211d · outbound
This paper cites Word Representations: A Simple and General Method for Semi-Supervised Learning.
Quantifying Mix Network Privacy Erosion with Generative Models Word Representations: A Simple and General Method for Semi-Supervised Learning
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.720403Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f1709cdc-f2b2-493c-acc2-b25f49ffbb9e · outbound
This paper cites Vuvuzela: Scalable private messaging resistant to traffic analysis.
Quantifying Mix Network Privacy Erosion with Generative Models Vuvuzela: Scalable private messaging resistant to traffic analysis
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.710438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6ea48306-9267-4bf3-af86-da18ab2d90b1 · outbound
This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.
Quantifying Mix Network Privacy Erosion with Generative Models Attention is all you need.Advances in neural information processing systems, 30, 2017
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.524787Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6869b661-fa0c-439c-a767-ad59b9ac114d · outbound
This paper cites Effective attacks and provable defenses for website fingerprinting.
Quantifying Mix Network Privacy Erosion with Generative Models Effective attacks and provable defenses for website fingerprinting
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.691630Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7bab3be8-5104-407f-8ab9-025b2321c138 · outbound
This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.
Quantifying Mix Network Privacy Erosion with Generative Models HuggingFace's Transformers: State-of-the-art Natural Language Processing
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.530780Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 782bd3e7-b9d2-4cef-8e04-0504b2feea86 · outbound
This paper cites Scalable anonymous group communication in the anytrust model.
Quantifying Mix Network Privacy Erosion with Generative Models Scalable anonymous group communication in the anytrust model
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.680777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 53e8d447-f267-49c8-921f-8f4a7444c543 · outbound
This paper cites Harnessing the power of llms in practice: A survey on chatgpt and beyond.
Quantifying Mix Network Privacy Erosion with Generative Models Harnessing the power of llms in practice: A survey on chatgpt and beyond
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:04:41.536871Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16dca343-9632-4e8a-92e2-c05508a7b19b · outbound
This paper cites A Survey of Large Language Models, 2023.
Quantifying Mix Network Privacy Erosion with Generative Models A Survey of Large Language Models, 2023
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:04:41.661298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Pith citing papers
No inbound Pith citation observations are available.