Paper Citation Record · LEDGER

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study

As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2502.06567.

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

pith.paper-citation-record.v1
2502.06567 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation 4471bd84-d1db-4162-b486-fa246ab99283 · outbound

This paper cites write newline.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study write newline

Reference 1

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Observation c95eacca-69c5-4ce0-9d9e-70e9398a7b9e · outbound

This paper cites Chemberta-2: Towards chemical foundation models, 2022.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Chemberta-2: Towards chemical foundation models, 2022

Reference 2

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Observation 6e6f3dc7-2822-477b-80ba-69615ed48011 · outbound

This paper cites and Gin \'e , E.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study and Gin \'e , E

Reference 3

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Observation ae770d1c-4ec4-486c-8130-50114dffdb0d · outbound

This paper cites Fundamental limits of membership inference attacks on machine learning models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Fundamental limits of membership inference attacks on machine learning models

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-08T15:08:47.224307Z

Source-reported events for the cited work

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Observation adabac49-6637-4de0-9f7c-4e5c3d809367 · outbound

This paper cites Hardware-aware dnn compression via diverse pruning and mixed-precision quantization.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Hardware-aware dnn compression via diverse pruning and mixed-precision quantization

Reference 5

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Observation a55d7a77-7431-45cf-bd5d-b38085e7bd47 · outbound

This paper cites Scalable methods for 8-bit training of neural networks.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Scalable methods for 8-bit training of neural networks

Reference 6

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Source-reported events for the cited work

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Observation 6ba6bd15-1541-430b-87b5-3fa80a33d26d · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 7

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Observation 41bb7b77-3096-4c52-9678-deb7b2c0f425 · outbound

This paper cites and Hutter, M.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study and Hutter, M

Reference 8

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Observation 12d3224d-51d4-4d13-ad58-8ed2fd44591c · outbound

This paper cites Membership inference attacks from first principles.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Membership inference attacks from first principles

Reference 9

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Source-reported events for the cited work

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Observation 7da90b78-51c9-4988-b511-5d27e32e2b9e · outbound

This paper cites Extracting training data from diffusion models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Extracting training data from diffusion models

Reference 10

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Source-reported events for the cited work

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Observation 7ad19900-f634-4775-ba05-22328b5e2be2 · outbound

This paper cites Privacy-aware compression for federated data analysis.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Privacy-aware compression for federated data analysis

Reference 11

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Source-reported events for the cited work

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Observation 1928c491-3f96-49e5-a854-a0f1fffe4d19 · outbound

This paper cites Moderate deviations and associated laplace approximations for sums of independent random vectors.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Moderate deviations and associated laplace approximations for sums of independent random vectors

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:08:47.531540Z

Source-reported events for the cited work

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Observation 6d65cf9b-49d4-49cd-9f4d-ff5b4e1afd7c · outbound

This paper cites Bounding information leakage in machine learning.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Bounding information leakage in machine learning

Reference 13

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Source-reported events for the cited work

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Observation 7e1acab1-67b1-4713-a33a-71ee01ed74b8 · outbound

This paper cites Large deviations techniques and applications.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Large deviations techniques and applications

Reference 14

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This paper cites and The PyTorch Lightning team.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study and The PyTorch Lightning team

Reference 15

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Observation 28366b13-2220-4cd5-9559-76a214b0fabf · outbound

This paper cites and Lao, Y.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study and Lao, Y

Reference 16

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This paper cites M., Madhukar, N.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study M., Madhukar, N

Reference 17

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Observation a1e612de-8912-486a-9f62-ea866b86efdf · outbound

This paper cites and Gray, R.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study and Gray, R

Reference 18

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Observation 24af3af1-9a06-4746-b567-0c26f3ee5504 · outbound

This paper cites W., and Keutzer, K.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study W., and Keutzer, K

Reference 19

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This paper cites A survey of low-bit large language models: Basics, systems, and algorithms.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study A survey of low-bit large language models: Basics, systems, and algorithms

Reference 20

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study K., Thompson, P., Ambite, J

Reference 21

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Observation be257e54-7962-4d9f-99e2-0944ea0d8d15 · outbound

This paper cites Measuring Unintended Memorisation of Unique Private Features in Neural Networks.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Measuring Unintended Memorisation of Unique Private Features in Neural Networks

Reference 22

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Unresolved cited work

Reference 23

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Observation 5304b5b7-db8b-445e-b9f7-69c479c499b1 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Training Compute-Optimal Large Language Models

Reference 24

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This paper cites S., and Zhang, X.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study S., and Zhang, X

Reference 25

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This paper cites W., Xiao, C., Sun, J., and Zitnik, M.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study W., Xiao, C., Sun, J., and Zitnik, M

Reference 26

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Observation 910b9220-e50e-4c44-9ec4-825d613b6621 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 27

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Observation ac74ad9d-6327-4765-98d5-0c0c6e06727a · outbound

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Scaling Laws for Neural Language Models

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Observation a18bd00a-24be-4b24-ae8c-4e2bffbe14fe · outbound

This paper cites Towards Model Quantization on the Resilience Against Membership Inference Attacks.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Towards Model Quantization on the Resilience Against Membership Inference Attacks

Reference 29

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Computer- Aided Prediction of Rodent Carcinogenicity by PASS and CISOC - PSCT

Reference 30

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Observation 133b5295-b12e-46ef-aaee-fa85b4c8211b · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 31

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Pre-training molecular graph representation with 3d geometry

Reference 32

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Observation 15978e8e-678e-4b5f-b03a-3e1a85f52cf5 · outbound

This paper cites \ ML-Doctor \ : Holistic risk assessment of inference attacks against machine learning models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study \ ML-Doctor \ : Holistic risk assessment of inference attacks against machine learning models

Reference 33

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Observation ff40a103-66df-49a8-b964-dbba660c3632 · outbound

This paper cites FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation

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This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 35

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This paper cites ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 36

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study v., Blankevoort, T., and Welling, M

Reference 37

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This paper cites A White Paper on Neural Network Quantization.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study A White Paper on Neural Network Quantization

Reference 38

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Overcoming oscillations in quantization-aware training

Reference 39

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Observation fbf7efb9-5a0b-477a-8cf1-3038a8504c3e · outbound

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study In-Distribution Consistency Regularization Improves the Generalization of Quantization-Aware Training

Reference 40

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This paper cites White-box vs black-box: Bayes optimal strategies for membership inference.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study White-box vs black-box: Bayes optimal strategies for membership inference

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:08:47.316618Z

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Observation c0bca22b-5269-4d79-ae9b-1480257985d6 · outbound

This paper cites Membership inference attacks against machine learning models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Membership inference attacks against machine learning models

Reference 42

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Validating adme qsar models using marketed drugs

Reference 43

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study and Raghunathan, A

Reference 44

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Machine learning models that remember too much

Reference 45

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This paper cites Beyond Memorization: Violating Privacy Via Inference with Large Language Models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Beyond Memorization: Violating Privacy Via Inference with Large Language Models

Reference 46

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This paper cites 3D Infomax improves GNNs for Molecular Property Prediction.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study 3D Infomax improves GNNs for Molecular Property Prediction

Reference 47

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study and Piantanida, P

Reference 48

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Observation f4d5ccf4-0c76-4cb7-a918-a9048575d36f · outbound

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Unresolved cited work

Reference 49

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Source-reported events for the cited work

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Observation 5cf4885f-e370-47c6-8f1b-b1d570fd23f4 · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 50

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This paper cites A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

Reference 51

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This paper cites Ladder: Enabling efficient \ Low-Precision \ deep learning computing through hardware-aware tensor transformation.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Ladder: Enabling efficient \ Low-Precision \ deep learning computing through hardware-aware tensor transformation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:08:47.249987Z

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Observation 74ed1d44-007a-41da-8056-89e6d5e473b9 · outbound

This paper cites Killing two birds with one stone: Quantization achieves privacy in distributed learning.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Killing two birds with one stone: Quantization achieves privacy in distributed learning

Reference 53

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Source-reported events for the cited work

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This paper cites Randomized Quantization is All You Need for Differential Privacy in Federated Learning.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Randomized Quantization is All You Need for Differential Privacy in Federated Learning

Reference 54

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This paper cites ViT-1.58b: Mobile Vision Transformers in the 1-bit Era.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study ViT-1.58b: Mobile Vision Transformers in the 1-bit Era

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T15:08:46.295540Z

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Observation 0d773aca-fed5-4a1e-8759-5a1153c86cf0 · outbound

This paper cites ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T15:08:46.300864Z

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Observation d983309a-48d6-4928-8ca9-6ba9b929e548 · outbound

This paper cites A survey on model compression for large language models.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study A survey on model compression for large language models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T15:08:46.306358Z

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Pith citing papers

Observation ed7c5182-a069-4a4f-a6ae-659dec6c2b29 · inbound

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization cites this paper.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.716504Z

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