The Privacy Blanket of the Shuffle Model

Calibrating Noise to Sensitivity in Private Data Analysis

Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272

Proceedings of the 25th international conference on Machine learning - ICML '08

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Probability theory the logic of science

Jaynes, E. T., & Bretthorst, G. L. (2003) · Cited by 4,600

Probability Theory: The Logic of Science

Jaynes, E. T. (2003) · doi:10.1017/cbo9780511790423 · Cited by 4,045

RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

Erlingsson, Ú., Pihur, V., & Korolova, A. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660348 · Cited by 1,838

Concentration Inequalities: A Nonasymptotic Theory of Independence

Boucheron, S., Lugosi, G., & Massart, P. (2013) · doi:10.1093/acprof:oso/9780199535255.001.0001 · Cited by 1,768

Lectures on the Coupling Method

Lindvall, T. (1992) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,160

Collecting Telemetry Data PrivatelyPreprint

Ding, B., Kulkarni, J., & Yekhanin, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.01524 · Cited by 519

Protection Against Reconstruction and Its Applications in Private Federated LearningPreprint

Bhowmick, A., Duchi, J. C., Freudiger, J., Kapoor, G., & Rogers, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00984 · Cited by 420

Prochlo: Strong Privacy for Analytics in the Crowd

Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J., & Seefeld, B. (2017) · Proceedings of the 26th Symposium on Operating Systems Principles · doi:10.1145/3132747.3132769 · Cited by 378

Frequency Estimation in the Shuffle Model with Almost a Single Message

Luo, Q., Wang, Y., & Yi, K. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560608 · Cited by 8

Research Challenges in Designing Differentially Private Text Generation Mechanisms

Feyisetan, O., Aggarwal, A., Xu, Z., & Teissier, N. (2021) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.v34i1.128461 · Cited by 8

Privacy-Enhanced Federated Learning: A Restrictively Self-Sampled and Data-Perturbed Local Differential Privacy Method

Zhao, J., Yang, M., Zhang, R., Song, W., Zheng, J., Feng, J., & Matwin, S. (2022) · Electronics · doi:10.3390/electronics11234007 · Cited by 10

Differential privacy in the shuffle model

Cheu, A. (2021) · doi:10.17760/d20409473 · Cited by 9

Differential Privacy in the Shuffle Model: A Survey of SeparationsPreprint

Cheu, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11839 · Cited by 8

A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning

Tiwari, K., Shukla, S., & George, J. P. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-4486-3_3 · Cited by 6

Private Read-Update-Write With Controllable Information Leakage for Storage-Efficient Federated Learning With Top r Sparsification

Vithana, S., & Ulukuş, Ş. (2023) · IEEE Transactions on Information Theory · doi:10.1109/tit.2023.3339450 · Cited by 10

Centering Policy and Practice: Research Gaps Around Usable Differential Privacy

Cummings, R., & Sarathy, J. (2023) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa58951.2023.00024 · Cited by 11

Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex LossesPreprint

Lowy, A. M., & Razaviyayn, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09779 · Cited by 7

Locally Private Streaming Data Release with Shuffling and Subsampling

Li, X., Cao, Y., & Yoshikawa, M. (2023) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icdew58674.2023.00026 · Cited by 6

Analyzing the Shuffle Model Through the Lens of Quantitative Information Flow

Jurado, M., Gonze, R. G., Alvim, M. S., & Catuscia Palamidessi, A. (2023) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf57540.2023.00033 · Cited by 6

The Power of Synergy in Differential Privacy: Combining a Small Curator with Local RandomizersPreprint

Beimel, A., Korolova, A., Kobbi Nissim, B., Sheffet, O., & Stemmer, U. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.itc.2020.14 · Cited by 6

UDFed: A Universal Defense Scheme for Various Poisoning Attacks on Federated Learning

Deng, J., Li, C., Zhang, N., Yang, J., & Gao, J. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3611126 · Cited by 5

Approximate Distance-Comparison-Preserving Symmetric Encryption

Fuchsbauer, G., Ghosal, R., Hauke, N., & O’Neill, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14791-3_6 · Cited by 4

Private and Secure Distributed Deep Learning: A Survey

Allaart, C. G., Amiri, S., Bal, H. E., Belloum, A. S. Z., Gommans, L., van Halteren, A., & Klous, S. (2024) · ACM Computing Surveys · doi:10.1145/3703452 · Cited by 9

EPS2: Privacy Preserving Set-Valued Data Analysis in the Shuffle Model

Wang, L., Ye, Q., Hu, H., & Meng, X. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3341171 · Cited by 5

Spreading the Privacy Blanket:

Gordon, D., Katz, J. I., Liang, M., & Xu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09234-3_25 · Cited by 5

Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocolsPreprint

Bellet, A., Guerraoui, R., & Hendrikx, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07138 · Cited by 5

Improved Differentially Private Decentralized Source Separation for fMRI DataPreprint

Imtiaz, H., Mohammadi, J. A., Silva, R. F., Baker, B. T., Plis, S., Sarwate, A. D., & Calhoun, V. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12913 · Cited by 5

Tight Accounting in the Shuffle Model of Differential PrivacyPreprint

Koskela, A., Heikkilä, M., & Honkela, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00477 · Cited by 5

Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation Under Differential Privacy

Murakami, T., Sei, Y., & Eriguchi, R. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00019 · Cited by 4

Locally Random Sampling for Practical Privacy Protection in Federated Learning

Wang, W., Liu, S., Liu, A., Liang, C., & Yu, S. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001571 · Cited by 4

A Theory of Composition for Differential Obliviousness

Zhou, M., Shi, E., Chan, T.-H. H., & Maimon, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30620-4_1 · Cited by 4

DPrio: Efficient Differential Privacy with High Utility for Prio

Keeler, D., Komlo, C., Lepert, E., Veitch, S., & He, X. G. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0086 · Cited by 6

Toward the Tradeoffs Between Privacy, Fairness and Utility in Federated Learning

Sun, K., Zhang, X., Lin, X., Li, G., Wang, J., & Li, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-99-9614-8_8 · Cited by 6

Rényi Differential Privacy in the Shuffle Model: Enhanced Amplification Bounds

Chen, E., Cao, Y., & Ge, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446115 · Cited by 3

A Range Query Scheme for Spatial Data with Shuffled Differential Privacy

Li, K., Hua, Z., Xu, Y., & Liu, Z. (2024) · Mathematics · doi:10.3390/math12131934 · Cited by 4

Optimal Locally Private Data Stream Analytics

Wang, S., Peng, Y., Chen, K., & Yang, W. (2024) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom52122.2024.10621194 · Cited by 4

ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security

Xie, C., Koyejo, S., & Gupta, I. (2022) · Algorithms · doi:10.3390/a15070233 · Cited by 5

Differential privacy with partial knowledgePreprint

Desfontaines, D., Mohammadi, E., Krahmer, E., & Basin, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00650 · Cited by 4

Shuffled Differentially Private Federated Learning for Time Series Data Analytics

Huang, C., Jiang, C., & Chen, Z. (2023) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea58696.2023.10241529 · Cited by 3

Communication-privacy-accuracy trade-offs in federated learning for non-IID data with shuffle model

Xiao, D., Fan, X., Chen, L., Li, M., & Zhang, M. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113872 · Cited by 3

PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning

Peleg, O., Lang, N., Ben-Ami, D., Rini, S., Shlezinger, N., & Cohen, K. (2025) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2025.3627252 · Cited by 5

Federated learning with bilateral defense via blockchain

Xiao, J., Nie, H., Yi, Z., Tang, X., & Lu, S. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107199 · Cited by 4

RM 2 : Answer Counting Queries Efficiently under Shuffle Differential Privacy

Luo, Q., Yu, J., Dong, W., Xu, Q., Yang, C., & Yi, K. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725415 · Cited by 3

Maintaining Privacy and Utility in IoT System Analytics

Mossé, D., Pötter, H., & Lee, S. (2020) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa50397.2020.00030 · Cited by 4

Distributed User-Level Private Mean Estimation

Girgis, A. M., Data, D., & Diggavi, S. N. (2022) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit50566.2022.9834713 · Cited by 4

Total variation distance privacy: Accurately measuring inference attacks and improving utility

Jia, J., Tan, C., Liu, Z., Li, X., Liu, Z., Lv, S., & Dong, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.037 · Cited by 3

Differentially Private Stochastic Linear Bandits: (Almost) for Free

Hanna, O. A., Girgis, A. M., Fragouli, C., & Diggavi, S. N. (2024) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2024.3389954 · Cited by 3

PP-CSA: Practical Privacy-Preserving Software Call Stack Analysis

Wang, Z., Ma, P., Wang, H., & Wang, S. (2024) · Proceedings of the ACM on Programming Languages · doi:10.1145/3649856 · Cited by 4

PAUSE: Privacy-Aware Active User Selection for Federated Learning

Peleg, O., Lang, N., Rini, S., Shlezinger, N., & Cohen, K. (2025) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp49660.2025.10890399 · Cited by 4

MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably

Alon, B., Naor, M., Omri, E., & Stemmer, U. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-68397-8_3 · Cited by 3

Continuous Release of Data Streams under both Centralized and Local Differential PrivacyPreprint

Wang, T., Chen, J. Q., Zhang, Z., Su, D., Cheng, Y., Li, Z., Li, N., & Jha, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.11753 · Cited by 3

Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response

Biswas, S., Jung, K., & Catuscia Palamidessi, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-57537-2_27 · Cited by 2

Hybrid aggregation for federated learning under blockchain framework

Li, X., Wu, G., Yao, L., & Geng, S. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.06.009 · Cited by 2

Enhanced Privacy Bound for Shuffle Model with Personalized Privacy

Liu, Y., Liu, Y., Xiong, L., Gu, Y., & Chen, H. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679911 · Cited by 2

Side-Channel Attacks and New Principles in the Shuffle Model of Differential Privacy

Wang, S., Li, J., Dong, C., Li, J., Zhou, Z., Wang, D., & Wen, Z. A. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3571661 · Cited by 2

SFLES: Shuffled differentially private federated learning with early-stopping strategy

Li, Y., Huang, C., Zhao, Y., Du, X., Huang, J., & Yuan, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129970 · Cited by 2

DPAR: High-Performance, Secure, and Scalable Differential Privacy-based AllReduce

Qi, H., Chen, W., Wang, C., & Lu, X. (2025) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3712285.3759828 · Cited by 2

Dynamic Private Subnetwork Selection for Personalized Federated Learning

Xin, B., Liu, J., Liu, X., Ding, K., Yang, R., Wang, Y., & Chen, S. (2025) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa67752.2025.00106 · Cited by 2

The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model

Kobbi Nissim, B., & Yan, C. (2022) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.805 · Cited by 2

Privacy-Preserving SGD on Shuffle Model

Zhang, L., & Zhang, H. F. (2023) · Journal of Mathematics · doi:10.1155/2023/4055950 · Cited by 2

Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions

Chan, T.-H. H., Xie, H. F., & Zhao, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28998 · Cited by 2

Differentially Private Multi-Site Treatment Effect Estimation

Koga, T., Chaudhuri, K., & Page, D. I. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00030 · Cited by 6

Shuffle Private Decentralized Convex Optimization

Zhang, L., & Zhang, H. F. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3405183 · Cited by 2

Fedrtid: an efficient shuffle federated learning via random participation and adaptive time constraint

Yang, Q., Du, X., Wu, X., Wang, W., Liu, A., & Wang, S. (2024) · Cybersecurity · doi:10.1186/s42400-024-00293-x · Cited by 2

Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning

Wang, S., Zeng, S., Li, J., Huang, S., & Chen, Y. (2025) · Applied Sciences · doi:10.3390/app15031595 · Cited by 2

From Bounded to Unbounded: Privacy Amplification via Shuffling with Dummies

Takagi, S., Kato, F., Cao, Y., & Yoshikawa, M. (2023) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf57540.2023.00034 · Cited by 2

Non-Interactive Anonymous Router with Quasi-Linear Router Computation

Fernando, R., Shi, E., Soni, P., Vanjani, N., & Waters, B. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48621-0_3 · Cited by 2

Frequency Estimation Under Multiparty Differential Privacy: One-shot and StreamingPreprint

Huang, Z., Qiu, Y., Yi, K., & Cormode, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01808 · Cited by 2

A Shuffling Framework for Local Differential PrivacyPreprint

Meehan, C., Chowdhury, A. R., Chaudhuri, K., & Jha, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06603 · Cited by 2

Selective MPC: Distributed Computation of Differentially Private Key-Value StatisticsPreprint

Humphries, T., Mahdavi, R. A., Veitch, S., & Kerschbaum, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.12407 · Cited by 2

Nebula: Efficient, Private and Accurate Histogram Estimation

Shamsabadi, A. S., Snyder, P., Giles, R., Bellet, A., & Haddadi, H. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3744789 · Cited by 1

Measuring Local and Shuffled Privacy of Gradient Randomized Response

Matsumoto, M., Takahashi, T., Liew, S. P., & Oguchi, M. (2024) · Journal of Information Processing · doi:10.2197/ipsjjip.32.990 · Cited by 1

FLDS: differentially private federated learning with double shufflers

Qi, Q., Yang, X., Hu, C., Tang, P., Su, Z., & Guo, S. (2024) · The Computer Journal · doi:10.1093/comjnl/bxae122 · Cited by 1

A Differential Privacy Framework with Adjustable Efficiency–Utility Trade-Offs for Data Collection

Kim, J. W., & Cho, S.-H. (2025) · Mathematics · doi:10.3390/math13050812 · Cited by 1

Differential Private Data Stream Analytics in the Local and Shuffle Models

Wang, S., Li, J., Peng, Y., Chen, K., Yang, W., Jiang, H. L., & Li, J. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3559621 · Cited by 1

Efficient Byzantine-Robust Federated Learning Based on the Multimessage Shuffle Protocol for Consumer Internet of Things

Jin, C., Li, L., Wang, J., Li, J., Liu, J., Chen, G., Zhang, H., & Weng, J. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3567098 · Cited by 1

SSDP-DCA: Enhancing Data Collaboration Analysis With Privacy Amplification Techniques in Cloud Environments

Sun, T., Ye, X., Imakura, A., Omote, K., & Sakurai, T. (2026) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2026.3680131 · Cited by 1

Defense against Poisoning Attacks under Shuffle-DP

Wang, S., Luo, Q., Hu, Y., Wang, L., Xu, Q., Yang, C., Qin, Z., Ren, K., & Dong, W. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3786638 · Cited by 1

Analysis of Shuffling Beyond Pure Local Differential Privacy

Takagi, S., & Liew, S. P. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3801892 · Cited by 1

Setup Protocols for Sender Anonymity

Huang, T., Zhang, J., & Ando, M. (2026) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf68417.2026.00020 · Cited by 1

Differentially Private Probabilistic Social Choice in the Shuffle Model

Ding, Q., Sun, K., Jiang, L., Zhou, H., & Zhou, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46775-2_4 · Cited by 1

FL-HSDP: Client-Side Defense for Federated Learning via Hessian Optimization and Shuffled Differential Privacy

Li, Y., Wang, G., Wang, T., & Jia, W. (2023) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys60770.2023.00099 · Cited by 1

Multidimensional categorical data collection under shuffled differential privacy

Wang, N., Zhuang, J., Wang, Z., Wei, Z., Gu, Y., Tang, P., & Yu, G. (2025) · Computers & Security · doi:10.1016/j.cose.2024.104301 · Cited by 1

Privacy Amplification Via Bernoulli SamplingPreprint

Imola, J., & Chaudhuri, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.10594 · Cited by 1

Exploring Intermediate Paradigms: A Comparative Analysis of Shuffle and Pan-Private Models in Differential Privacy with Emphasis on Trust Levels, Engineering, and Mathematical PerspectivesPreprint

Diyora, V., & Savani, N. (2023) · Research Square · doi:10.21203/rs.3.rs-3626763/v1 · Cited by 1

Intertwining Order Preserving Encryption and Differential Privacy.Preprint

Chowdhury, A. R., Ding, B., Jha, S., Liu, W., & Zhou, J. (2020) · arXiv (Cornell University)

Privacy Enhancement via Dummy Points in the Shuffle ModelPreprint

Li, X., Liu, W., Feng, H., Huang, K., Liu, J., Ren, K., & Qin, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13738

Infinitely Divisible Noise in the Low Privacy Regime

Pagh, R., & Stausholm, N. M. (2021) · Research at the University of Copenhagen (University of Copenhagen) · doi:10.48550/arxiv.2110.06559

RECENT PROGRESS OF DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH THE SHUFFLE MODEL

Ahmed, M. A. M., Wu, S., Dushime, L. D., & Tao, Y.-H. (2021) · International Journal of Engineering Technologies and Management Research · doi:10.29121/ijetmr.v8.i11.2021.1028

Efficiency-optimized Data Perturbation in Local Differentially Private Federated LearningPreprint

Zhao, J., Yang, M., Zheng, J., Feng, J., & Matwin, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1890894/v1

Tight differential privacy blanket for shuff model

Biswas, S., Jung, K., & Catuscia Palamidessi, A. (2022) · IET conference proceedings. · doi:10.1049/icp.2022.2041

Anonymous Communication and Shuffle Model in Federated Learning

Yu, S., & Cui, L. (2022) · Digital Privacy and Security · doi:10.1007/978-981-19-8692-5_8

Amplification by Shuffling without Shuffling

Balle, B., Bell, J., & Gascón, A. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623215

Fedrti: Differential Privacy Federated Learning Via Random Participation and Time Constraints for Network CongestionPreprint

Yang, Q., Du, X., Wu, X., Wang, N., Wang, W., & Liu, A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4646455

Personalized Differential Privacy in the Shuffle Model

Yang, R., Yang, H. Y., Fan, J., Dong, C., Pang, Y., Wong, D. S., & Wang, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-99-9785-5_33

AI Approach Towards Optimal Finding of Renewable Sources of Energy and Their Classification

Saha, P., Mukherjee, A., & Mukherjee, G. (2024) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-2355-7.ch005

Privacy-Preserving Frank-Wolfe on Shuffle Model

Zhang, L., Wu, S., & Zhang, H. F. (2024) · Acta Mathematicae Applicatae Sinica English Series · doi:10.1007/s10255-024-1095-6

Lightweight Byzantine-Robust and Privacy-Preserving Federated Learning

Lu, Z. G., Lu, S., Cui, Y., Wu, J., Nie, H., Xiao, J., & Yi, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-69766-1_19

Histogram Publication with Shuffled Differential Privacy

Gao, Y., Yang, X., Wu, T. S., & Zhou, Y. (2024) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c63300.2024.00093

Efficient Privacy-Preserving Federated Learning with Byzantine Robustness

Jin, C., Li, L., Wang, J., Sun, Y., Jin, Y., & Chen, G. (2024) · IEEE International Conference on Cognitive Computing and Complex Data (ICCD) · doi:10.1109/iccd62811.2024.10843471

Secure multi-party shuffling with optimal communication

Chen, K.-C., Pang, B., Li, Y., & Wang, M. (2025) · The Computer Journal · doi:10.1093/comjnl/bxaf043

Reidentification risks in distributed differentially private data leveraging large language models

Sei, Y., Okumura, H., Ohsuga, A., & Imoize, A. L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34109-0.00007-3

Improving Utility of Private Join Size Estimation via Shuffling

Liu, X., Mao, Y., Zhang, M., & Li, M. (2025) · Mathematics · doi:10.3390/math13213468

Efficient Privacy-Preserving Streaming Histogram Publication via Sampling-Based Shuffled Method

Zheng, X., Jin, Y., Wang, X., & Liu, Z. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010063

AdaCOS: adaptive differential privacy shuffle model based on cosine similarity

Ye, Z., Wang, G., Ma, C., Guo, Z., & Huang, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00397-5

Hole-Free Differentially Private Multiparty Laplace Mechanism

Zarei, A., & Vinterbo, S. A. (2026) · SN Computer Science · doi:10.1007/s42979-025-04579-w

Secure Hierarchical Asynchronous Federated Learning with Shuffle Model and Mask–DP

Chen, Y., Ai, D., & Yan, L. (2026) · Sensors · doi:10.3390/s26020617

Answering Range Queries for Arbitrary Distribution Under Shuffled Differential Privacy

Wang, L., Ye, Q., Hu, H., & Meng, X. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3657305

Private Summation in the Multi-Message Shuffle Model

Balle, B., Bell, J., Gascón, A., & Kobbi Nissim, B. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417242 · Cited by 97

Differential Privacy in the Shuffle Model: A Survey of SeparationsPreprint

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