The Privacy Blanket of the Shuffle Model

Calibrating Noise to Sensitivity in Private Data Analysis

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Probability theory the logic of science

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Probability Theory: The Logic of Science

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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

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Lectures on the Coupling Method

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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

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772

Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey

Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 439

LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy

Sun, L., Qian, J., & Chen, X. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/217 · Cited by 267

Local differential privacy and its applications: A comprehensive survey

Yang, M., Guo, T., Zhu, T., Tjuawinata, I., Zhao, J., & Lam, K. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103827 · Cited by 308

A Field Guide to Federated OptimizationPreprint

Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., y Arcas, B. A., Al-Shedivat, M., Andrew, G., Avestimehr, S., Daly, K., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.06917 · Cited by 467

The distributed discrete gaussian mechanism for federated learning with secure aggregation

Kairouz, P., Liu, Z., & Steinke, T. (2021) · arXiv · doi:10.48550/arxiv.2102.06387 · Cited by 117

Estimating Numerical Distributions under Local Differential Privacy

Li, Z., Wang, T., Lopuhaä-Zwakenberg, M., Li, N., & Škorić, B. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3389700 · Cited by 120

FLAME: Differentially Private Federated Learning in the Shuffle Model

Liu, R., Cao, Y., Chen, H., Guo, R., & Yoshikawa, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17053 · Cited by 111

Privacy in Deep Learning: A Survey

Mireshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., & Esmaeilzadeh, H. (2020) · arXiv · doi:10.48550/arxiv.2004.12254 · Cited by 106

A Comprehensive Survey on Local Differential Privacy

Xiong, X., Liu, S., Li, D., Cai, Z., & Niu, X. (2020) · Security and Communication Networks · doi:10.1155/2020/8829523 · Cited by 110

Differentially Private Byzantine-Robust Federated Learning

Ma, X., Sun, X., Wu, Y., Liu, Z., Chen, X., & Dong, C. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3167434 · Cited by 94

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

Ulukuş, Ş., Avestimehr, S., Gastpar, M., Jafar, S. A., Tandon, R., & Tian, C. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3142358 · Cited by 92

Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling

Feldman, V., McMillan, A., & Talwar, K. (2022) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs52979.2021.00096 · Cited by 122

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

Breaking the Communication-Privacy-Accuracy Trilemma

Chen, W., Kairouz, P., & Özgür, A. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3218772 · Cited by 87

Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey

Husnoo, M. A., Anwar, A., Chakrabortty, R. K., Doss, R., & Ryan, M. J. (2021) · IEEE Access · doi:10.1109/access.2021.3124309 · Cited by 74

PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data

Zhou, J., Chen, S., Wu, Y., Li, H., Zhang, B., Zhou, L., Hu, Y., Xiang, Z., Li, Z., Chen, N., et al. (2024) · Science Advances · doi:10.1126/sciadv.adh8601 · Cited by 63

Scalable and Differentially Private Distributed Aggregation in the Shuffled Model

Ghazi, B., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1906.08320 · Cited by 62

Federated Heavy Hitters Discovery with Differential PrivacyPreprint

Zhu, W., Kairouz, P., McMahan, H. B., Sun, H., & Li, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08534 · Cited by 66

Differentially Private Federated Learning on Heterogeneous DataPreprint

Noble, M., Bellet, A., & Dieuleveut, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09278 · Cited by 60

Exploring privacy measurement in federated learning

Jagarlamudi, G. K., Yazdinejad, A., Parizi, R. M., & Pouriyeh, S. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-023-05846-4 · Cited by 52

The Role of Interactivity in Local Differential Privacy

Joseph, M., Mao, J., Neel, S., & Roth, A. (2019) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2019.00015 · Cited by 61

Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs

Girgis, A. M., Data, D., Diggavi, S. N., Kairouz, P., & Suresh, A. T. (2021) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2021.3056102 · Cited by 80

Private Aggregation from Fewer Anonymous Messages

Ghazi, B., Manurangsi, P., Pagh, R., & Velingker, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45724-2_27 · Cited by 50

Blinder -- Scalable, Robust Anonymous Committed Broadcast

Abraham, I., Benny Pinkas, B., & Yanai, A. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417261 · Cited by 43

Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical EvaluationPreprint

Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Song, S., Talwar, K., & Thakurta, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.03618 · Cited by 85

Privacy-preserving federated mining of frequent itemsets

Chen, Y., Gan, W., Wu, Y., & Yu, P. S. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.002 · Cited by 42

On the Rényi Differential Privacy of the Shuffle Model

Girgis, A. M., Data, D., Diggavi, S. N., Suresh, A. T., & Kairouz, P. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484794 · Cited by 46

A Multi-Shuffler Framework to Establish Mutual Confidence for Secure Federated Learning

Zhou, Z., Xu, C., Wang, M., Kuang, X., Zhuang, Y., & Yu, S. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3215574 · Cited by 40

Improving utility and security of the shuffler-based differential privacy

Wang, T., Ding, B., Xu, M., Huang, Z., Hong, C., Zhou, J., Li, N., & Jha, S. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3424573.3424576 · Cited by 38

DP-cryptography

Wagh, S., He, X., Machanavajjhala, A., & Mittal, P. (2021) · Communications of the ACM · doi:10.1145/3418290 · Cited by 38

How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?

Elkordy, A. R., Zhang, J., Ezzeldin, Y. H., Psounis, K., & Avestimehr, S. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0030 · Cited by 55

Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model

Imola, J., Murakami, T., & Chaudhuri, K. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560659 · Cited by 33

Joint Top-K Sparsification and Shuffle Model for Communication-Privacy-Accuracy Tradeoffs in Federated-Learning-Based IoV

Sun, K., Xu, H., Hua, K., Lin, X., Li, G., Jiang, T., & Li, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3370991 · Cited by 33

On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy

Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77883-5_16 · Cited by 33

The right complexity measure in locally private estimation: It is not the Fisher information

Duchi, J. C., & Ruan, F. (2024) · The Annals of Statistics · doi:10.1214/22-aos2227 · Cited by 54

LinkedIn's Audience Engagements API

Rogers, R., Subramaniam, S., Peng, S. X., Durfee, D., Lee, S., Kancha, S. K., Sahay, S., & Ahammad, P. (2021) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.782 · Cited by 90

Path Oblivious Heap: Optimal and Practical Oblivious Priority Queue

Shi, E. (2020) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40000.2020.00037 · Cited by 29

LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at ScalePreprint

Rogers, R., Subramaniam, S., Peng, S. X., Durfee, D., Lee, S., Kancha, S. K., Sahay, S., & Ahammad, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05839 · Cited by 29

Scenario-based Adaptations of Differential Privacy: A Technical Survey

Zhao, Y., Du, J. T., & Chen, J. (2024) · ACM Computing Surveys · doi:10.1145/3651153 · Cited by 28

Random Sampling Plus Fake Data

Arcolezi, H. H., Couchot, J.-F., Bouna, B. A., & Xiao, X. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482467 · Cited by 27

Differentially Private Speaker Anonymization

Shamsabadi, A. S., Srivastava, B. M. L., Bellet, A., Vauquier, N., Emmanuel Vincent, A., Maouche, M., Tommasi, M., & Papernot, N. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0007 · Cited by 27

Distributed, Private, Sparse Histograms in the Two-Server Model

Bell, J., Gascón, A., Ghazi, B., Kumar, R., Manurangsi, P., Raykova, M., & Schoppmann, P. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3559383 · Cited by 27

Differentially Private Summation with Multi-Message ShufflingPreprint

Balle, B., Bell, J., Gascón, A., & Kobbi Nissim, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09116 · Cited by 26

On the Power of Multiple Anonymous Messages

Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1908.11358 · Cited by 26

Differentially Private Federated Learning: An Information-Theoretic Perspective

Asoodeh, S., Chen, W., Calmon, F. P., & Özgür, A. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9518124 · Cited by 25

A Game-Theoretic Federated Learning Framework for Data Quality Improvement

Zhang, L., Zhu, T., Xiong, P., Zhou, W., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3230959 · Cited by 25

Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition

Novikova, E., Fomichov, D., Kholod, I., & Filippov, E. (2022) · Sensors · doi:10.3390/s22082983 · Cited by 24

Differentially Private Histograms in the Shuffle Model from Fake Users

Cheu, A., & Zhilyaev, M. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833614 · Cited by 24

Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification

Vithana, S., & Ulukuş, Ş. (2023) · IEEE Transactions on Information Theory · doi:10.1109/tit.2023.3292187 · Cited by 24

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation

Bagdasaryan, E., Kairouz, P., Mellem, S., Gascón, A., Bonawitz, K., Estrin, D. L., & Gruteser, M. (2022) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2022-0104 · Cited by 38

Building Trusted Federated Learning: Key Technologies and Challenges

Chen, D., Jiang, X., Zhong, H., & Cui, J. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12010013 · Cited by 21

Privacy-Preserving Collaborative Data Collection and Analysis with Many Missing Values

Sei, Y., Andrew, J., Okumura, H., & Ohsuga, A. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3174887 · Cited by 19

Machine Learning Model Generation With Copula-Based Synthetic Dataset for Local Differentially Private Numerical Data

Sei, Y., Onesimu, J. A., & Ohsuga, A. (2022) · IEEE Access · doi:10.1109/access.2022.3208715 · Cited by 19

Shuffle Differential Private Data Aggregation for Random Population

Wang, S., Luo, X., Qian, Y., Zhu, Y., Chen, K., Chen, Q., Xin, B., & Yang, W. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3247541 · Cited by 20

Local differentially private federated learning with homomorphic encryption

Zhao, J., Huang, C., Wang, W., Xie, R., Dong, R., & Matwin, S. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05378-x · Cited by 22

On Distributed Differential Privacy and Counting Distinct ElementsPreprint

Chen, L., Ghazi, B., Kumar, R., & Manurangsi, P. (2020) · arXiv (Cornell University) · doi:10.4230/lipics.itcs.2021.56 · Cited by 18

Local Differential Privacy for Regret Minimization in Reinforcement LearningPreprint

Garcelon, E., Perchet, V., Pike-Burke, C., & Pirotta, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07778 · Cited by 24

Interactive Proofs For Differentially Private Counting

Biswas, A., & Cormode, G. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3616681 · Cited by 17

Automatic Tuning of Privacy Budgets in Input-Discriminative Local Differential Privacy

Murakami, T., & Sei, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3267082 · Cited by 16

Differential Privacy-Preserving of Multi-Party Collaboration Under Federated Learning in Data Center Networks

Wang, X., Fan, W., Hu, X., He, J., & Chi, C. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3341299 · Cited by 18

Privacy Amplification by Mixing and Diffusion MechanismsPreprint

Balle, B., Barthe, G., Gaboardi, M., & Geumlek, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12264 · Cited by 16

Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy

Xu, S., Zheng, Y., & Hua, Z. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690200 · Cited by 21

Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

Liu, Y., Zhao, S., Xiong, L., Liu, Y., & Chen, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i10.26400 · Cited by 17

Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning

Lyu, L. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207618 · Cited by 15

Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed LearningPreprint

Girgis, A. M., Data, D., & Diggavi, S. N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08763 · Cited by 15

Unknown Worker Recruitment With Long-Term Incentive in Mobile Crowdsensing

Zhou, Q., Zhang, X., & Yang, Z. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3471569 · Cited by 14

A Correlated Noise-Assisted Decentralized Differentially Private Estimation Protocol, and its Application to fMRI Source Separation

Imtiaz, H., Mohammadi, J. A., Silva, R. F., Baker, B. T., Plis, S., Sarwate, A. D., & Calhoun, V. D. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3126546 · Cited by 20

Distributed Differential Privacy via Shuffling Versus Aggregation: A Curious Study

Wei, Y. X., Jia, J., Wu, Y., Hu, C., Dong, C., Liu, Z., Chen, X., Peng, Y., & Wang, S. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3351474 · Cited by 18

An accurate, scalable and verifiable protocol for federated differentially private averaging

Sabater, C., Bellet, A., & Ramon, J. (2022) · Machine Learning · doi:10.1007/s10994-022-06267-9 · Cited by 20

Secret Specification Based Personalized Privacy-Preserving Analysis in Big Data

Chen, J., Hu, C., Liu, Z., Xiang, T., Hu, P., & Yu, J. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3433433 · Cited by 14

Hiding Numerical Vectors in Local Private and Shuffled Messages

Wang, S., Li, J., Qian, Y., Du, J., Lin, W., & Yang, W. P. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/510 · Cited by 13

Differentially Private Federated Learning with Shuffling and Client Self-Sampling

Girgis, A. M., Data, D., & Diggavi, S. N. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9517906 · Cited by 13

Re-Identification in Differentially Private Incomplete Datasets

Sei, Y., Okumura, H., & Ohsuga, A. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3175999 · Cited by 13

An Algorithmic Framework For Differentially Private Data Analysis on Trusted ProcessorsPreprint

Allen, J., Ding, B., Kulkarni, J., Nori, H., Ohrimenko, O., & Yekhanin, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00736 · Cited by 13

Lossless Compression of Efficient Private Local RandomizersPreprint

Feldman, V., & Talwar, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.12099 · Cited by 13

Aggregation and Transformation of Vector-Valued Messages in the Shuffle Model of Differential Privacy

Scott, M., Cormode, G., & Maple, C. R. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3147643 · Cited by 12

Efficient protocols for heavy hitter identification with local differential privacy

Zhao, D., Zhao, S., Chen, H., Liu, R., Li, C., & Liang, W. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0412-y · Cited by 12

Privacy Enhancement Via Dummy Points in the Shuffle Model

Li, X., Liu, W., Feng, H., Huang, K., Hu, Y., Liu, J., Ren, K., & Qin, Z. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3263162 · Cited by 12

V2V-ESP: Vehicle-to-Vehicle Energy Sharing Privacy Protection Scheme Based on SDP Algorithm

Ju, Z., & Li, Y. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3321083 · Cited by 12

On the Round Complexity of the Shuffle Model

Beimel, A., Haitner, I., Kobbi Nissim, B., & Stemmer, U. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64378-2_24 · Cited by 12

Non-Interactive Anonymous Router

Shi, E., & Wu, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77883-5_17 · Cited by 12

Network Shuffling: Privacy Amplification via Random Walks

Liew, S. P., Takahashi, T., Takagi, S., Kato, F., Cao, Y., & Yoshikawa, M. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3526162 · Cited by 12

The limits of pan privacy and shuffle privacy for learning and estimation

Cheu, A., & Ullman, J. (2021) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3406325.3450995 · Cited by 12

Differentially Private Numerical Vector Analyses in the Local and Shuffle Model

Wang, S., Yu, S., Ren, X., Li, J., Li, Y., Yang, W., Yan, H., & Li, J. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3340178 · Cited by 11

AdaSTopk: Adaptive federated shuffle model based on differential privacy

Yang, Q., Du, X., Liu, A., Wang, N., Wang, W., & Wu, X. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119186 · Cited by 11

Locally Private k-Means in One RoundPreprint

Chang, A., Ghazi, B., Kumar, R., & Manurangsi, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09734 · Cited by 14

Differentially Private Multi-Armed Bandits in the Shuffle ModelPreprint

Tenenbaum, J., Kaplan, H. Y., Mansour, Y., & Stemmer, U. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02900 · Cited by 11

Differential Privacy Configurations in the Real World: A Comparative Analysis

Khavkin, M., & Toch, E. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3603731 · Cited by 11

Private Federated Submodel Learning with Sparsification

Vithana, S., & Ulukuş, Ş. (2022) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw54588.2022.9965815 · Cited by 10

Efficient Noise Generation Protocols for Differentially Private Multiparty Computation

Eriguchi, R., Ichikawa, A., Kunihiro, N., & Nuida, K. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3227568 · Cited by 10

Samplable Anonymous Aggregation for Private Federated Data Analysis

Talwar, K., Wang, S., McMillan, A., Feldman, V., Bansal, P., Basile, B., Cahill, Á., Chan, Y. S., Chatzidakis, M., Chen, J., et al. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690224 · Cited by 21

Efficient Noise Generation to Achieve Differential Privacy with Applications to Secure Multiparty Computation

Eriguchi, R., Ichikawa, A., Kunihiro, N., & Nuida, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-662-64322-8_13 · Cited by 10

Frequency estimation under multiparty differential privacy

Huang, Z., Qiu, Y., Yi, K., & Cormode, G. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3547305.3547312 · Cited by 10

Selective MPC

Humphries, T., Mahdavi, R. A., Veitch, S., & Kerschbaum, F. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560559 · Cited by 10

The power of the hybrid model for mean estimation

Avent, B., Dubey, Y., & Korolova, A. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0062 · Cited by 9

Collecting and analyzing key-value data under shuffled differential privacy

Wang, N., Zheng, W., Wang, Z., Wei, Z., Gu, Y., Tang, P., & Yu, G. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-022-1572-0 · Cited by 9

Separating Local & Shuffled Differential Privacy via HistogramsPreprint

Balcer, V., & Cheu, A. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.itc.2020.1 · Cited by 50

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Private Summation in the Multi-Message Shuffle Model

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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

Differential privacy in the shuffle model

(n.d.) · Digital Repository Service (Northeastern University)

Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message

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Distributed Differential Privacy via Shuffling Versus Aggregation: A Curious Study

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