Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models | Paper Citation Record

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This paper cites write newline.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models write newline

Reference 1

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Unresolved cited work

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This paper cites Exploring homomorphic encryption and differential privacy techniques towards secure federated learning paradigm.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Exploring homomorphic encryption and differential privacy techniques towards secure federated learning paradigm

Reference 3

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This paper cites Foundations of data science.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Foundations of data science

Reference 4

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This paper cites B., Patel, S., Ramage, D., Segal, A., and Seth, K.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models B., Patel, S., Ramage, D., Segal, A., and Seth, K

Reference 5

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This paper cites Efficient intent detection with dual sentence encoders.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Efficient intent detection with dual sentence encoders

Reference 6

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This paper cites H., and Shi, X.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models H., and Shi, X

Reference 7

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This paper cites Imagenet: A large-scale hierarchical image database.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Imagenet: A large-scale hierarchical image database

Reference 8

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 9

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This paper cites Collecting telemetry data privately.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Collecting telemetry data privately

Reference 10

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

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This paper cites Differential privacy.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Differential privacy

Reference 12

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This paper cites The algorithmic foundations of differential privacy.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models The algorithmic foundations of differential privacy

Reference 13

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This paper cites Rappor: Randomized aggregatable privacy-preserving ordinal response.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Rappor: Randomized aggregatable privacy-preserving ordinal response

Reference 14

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This paper cites Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses

Reference 15

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This paper cites Secure aggregation is insecure: Category inference attack on federated learning.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Secure aggregation is insecure: Category inference attack on federated learning

Reference 16

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This paper cites Deep residual learning for image recognition.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Deep residual learning for image recognition

Reference 17

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This paper cites J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W

Reference 18

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This paper cites S., and Zhang, X.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models S., and Zhang, X

Reference 19

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This paper cites D., Chen, A., Shila, D.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models D., Chen, A., Shila, D

Reference 20

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This paper cites E., Qureshi, M.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models E., Qureshi, M

Reference 21

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This paper cites Learning multiple layers of features from tiny images.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Learning multiple layers of features from tiny images

Reference 22

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Unresolved cited work

Reference 23

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This paper cites The power of scale for parameter-efficient prompt tuning.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models The power of scale for parameter-efficient prompt tuning

Reference 24

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This paper cites Tear: Exploring temporal evolution of adversarial robustness for membership inference attacks against federated learning.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Tear: Exploring temporal evolution of adversarial robustness for membership inference attacks against federated learning

Reference 25

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 26

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This paper cites Towards differentially private text representations.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Towards differentially private text representations

Reference 27

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This paper cites Flamingo: Multi-round single-server secure aggregation with applications to private federated learning.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Flamingo: Multi-round single-server secure aggregation with applications to private federated learning

Reference 28

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models E., Pham, P

Reference 29

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Unresolved cited work

Reference 30

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This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 31

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Secure aggregation is not private against membership inference attacks

Reference 32

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models and Thai, M

Reference 33

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models N., and Thai, M

Reference 34

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Unresolved cited work

Reference 35

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Fedshe: privacy preserving and efficient federated learning with adaptive segmented ckks homomorphic encryption

Reference 36

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Eluding secure aggregation in federated learning via model inconsistency

Reference 37

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Natural language understanding with privacy-preserving bert

Reference 38

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Improving language understanding by generative pre-training

Reference 39

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models K., Klambauer, G., Brandstetter, J., and Hochreiter, S

Reference 40

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models img2vec, 2021

Reference 41

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

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 42

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models T., Huang, Y.-H., Wu, J., and Chen, Y.-S

Reference 43

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Membership inference attacks against machine learning models

Reference 44

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models T., et al

Reference 45

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This paper cites A comprehensive survey on local differential privacy toward data statistics and analysis.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models A comprehensive survey on local differential privacy toward data statistics and analysis

Reference 46

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Unresolved cited work

Reference 47

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This paper cites Gradient leakage attacks in federated learning: Research frontiers, taxonomy and future directions.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Gradient leakage attacks in federated learning: Research frontiers, taxonomy and future directions

Reference 48

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Adapter is All You Need for Tuning Visual Tasks

Reference 49

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Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Unresolved cited work

Reference 50

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This paper cites B., Goldberg, Y., and Ravfogel, S.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models B., Goldberg, Y., and Ravfogel, S

Reference 51

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This paper cites Gan enhanced membership inference: A passive local attack in federated learning.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Gan enhanced membership inference: A passive local attack in federated learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:32:46.241658Z

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Observation 9fdfced0-b937-45a9-aaac-30fd46ee8e89 · outbound

This paper cites Character-level convolutional networks for text classification.

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models Character-level convolutional networks for text classification

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