RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482

Space/time trade-offs in hash coding with allowable errors

Bloom, B. (1970) · CACM · doi:10.1145/362686.362692 · Cited by 8,424

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

Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias

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Network Applications of Bloom Filters: A Survey

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Our Data, Ourselves: Privacy Via Distributed Noise Generation

Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835

Towards Sharp Inapproximability For Any 2-CSP

Austrin, P. (2007) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2007.41 · Cited by 1,340

A firm foundation for private data analysis

Dwork, C. (2010) · Communications of the ACM · doi:10.1145/1866739.1866758 · Cited by 856

No free lunch in data privacy

Kifer, D., & Machanavajjhala, A. (2011) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1989323.1989345 · Cited by 662

DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural NetworksPreprint

Hashemi, H., Wang, Y., & Annavaram, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01300 · Cited by 20

Instance-optimal Mean Estimation Under Differential PrivacyPreprint

Huang, Z., Liang, Y., & Yi, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00463 · Cited by 14

Antipodes of Label Differential Privacy: PATE and ALIBIPreprint

Malek, M., Mironov, I., Prasad, K., Shilov, I., & Tramèr, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03408 · Cited by 14

Optimal Compression of Locally Differentially Private MechanismsPreprint

Shah, A., Chen, W., Ballé, J., Kairouz, P., & Theis, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.00092 · Cited by 14

Private Hierarchical Clustering in Federated Networks

Kolluri, A., Baluta, T., & Saxena, P. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484822 · Cited by 15

Federated learning and differential privacy in clinical health: Extensive survey

Odera, D. (2023) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2023.8.2.0113 · Cited by 14

Privacy preservation in the internet of vehicles using local differential privacy and IOTA ledger

Iftikhar, Z., Anjum, A., Khan, A., Shah, M. A., & Jeon, G. (2023) · Cluster Computing · doi:10.1007/s10586-023-04002-0 · Cited by 13

Privacy-Preserving Federated Class-Incremental Learning

Xiao, J., Tang, X., & Lu, S. (2023) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2023.3344074 · Cited by 13

Sparsified federated learning with differential privacy for intrusion detection in VANETs based on Fisher Information Matrix

Chen, R., Chen, X., & Zhao, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301897 · Cited by 14

Privacy Amplification via Shuffling: Unified, Simplified, and Tightened

Wang, S., Peng, Y., Li, J., Wen, Z. A., Li, Z., Yu, S., Wang, D., & Yang, W. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3659437.3659444 · Cited by 14

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

Extremal Mechanisms for Pointwise Maximal Leakage

Grosse, L., Saeidian, S., & Oechtering, T. J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3449556 · Cited by 13

PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy

Wang, L., Ye, Q., Hu, H., & Meng, X. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3681954.3681981 · Cited by 13

Triangle Counting With Local Edge Differential Privacy

Eden, T., Liu, Q. C., Raskhodnikova, S., & Smith, A. (2025) · Random Structures and Algorithms · doi:10.1002/rsa.70002 · Cited by 13

PPBR: Privacy-Preserving and Byzantine-Robust Edge-Assisted Hierarchical Federated Learning in Mobile Networks

He, Y., Zhang, J., Yang, P., Sun, Z., & Shen, X. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3602911 · Cited by 13

Differential Privacy of Hierarchical Census Data: An Optimization Approach

Fioretto, F., & Hentenryck, P. V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30048-7_37 · Cited by 13

Publishing Video Data with Indistinguishable Objects

Wang, H., Hong, Y., Kong, Y., & Vaidya, J. S. (2020) · PubMed · doi:10.5441/002/edbt.2020.29 · Cited by 13

Private Post-GAN Boosting

Neunhoeffer, M., Wu, Z. S., & Dwork, C. (2020) · MADOC (University of Mannheim) · doi:10.48550/arxiv.2007.11934 · Cited by 13

Recommender Systems with Condensed Local Differential Privacy

Liu, A., Yao, Y., & Cheng, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62223-7_30 · Cited by 13

Towards Privacy-Preserving Deep Learning: Opportunities and Challenges

Ali, S., Irfan, M. M., Bomai, A., & Zhao, C. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00077 · Cited by 15

Private Release of Text Embedding Vectors

Feyisetan, O., & Kasiviswanathan, S. P. (2021) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2021.trustnlp-1.3 · Cited by 13

Radial Basis Function Network with Differential Privacy

Bugshan, N., Khalil, I., Moustafa, N., Almashor, M., & Abuadbba, A. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.013 · Cited by 13

Examining Compliance with Personal Data Protection Regulations in Interorganizational Data Analysis

Li, S.-C., Chen, Y., & Huang, Y. (2021) · Sustainability · doi:10.3390/su132011459 · Cited by 13

Current Trends in Data Summaries

Cormode, G. (2022) · ACM SIGMOD Record · doi:10.1145/3516431.3516433 · Cited by 13

XRand: Differentially Private Defense against Explanation-Guided Attacks

Nguyen, T., Lai, P., Phan, H., & Thai, M. T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i10.26401 · Cited by 13

Privacy-Preserving Outlier Detection for Data Streams

Böhler, J., Bernau, D., & Kerschbaum, F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61176-1_12 · Cited by 13

Continuous Location Statistics Sharing Algorithm with Local Differential Privacy

Errounda, F. Z., & Liu, Y. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8621876 · Cited by 13

Adversarial Attacks and Defenses in Malware Detection Classifiers

John, T. S., & Thomas, T. (2019) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-8407-0.ch007 · Cited by 13

PrivStream: Enabling Privacy-Preserving Inferences on IoT Data Stream at the Edge

Wang, D., Ren, J., Xu, C., Liu, J., Wang, Z., Zhang, Y., & Shen, X. (2019) · 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/smartcity/dss.2019.00180 · Cited by 13

A Locality Sensitive Hashing Based Approach for Federated Recommender System

Hu, H., Dobbie, G., Salcic, Z. A., Liu, M., Zhang, J., & Zhang, X. (2020) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid49817.2020.000-1 · Cited by 15

BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy

Sun, L., Ye, X., Zhao, J., Lu, C., & Yang, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59410-7_6 · Cited by 13

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

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

Embedding differential privacy in decision tree algorithm with different depths

Bai, X., Yao, J., Yuan, M., Deng, K., Xie, X., & Guan, H. (2017) · Science China Information Sciences · doi:10.1007/s11432-016-0442-1 · 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

Information-theoretic metrics for Local Differential Privacy protocolsPreprint

Lopuhaä-Zwakenberg, M., Škorić, B., & Li, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.07826 · 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

DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization

Jiang, H., Yu, H., Cheng, X., Pei, J., Pless, R. B., & Yu, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3265605 · Cited by 12

Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch

Lebeda, C. J., & Tětek, J. (2023) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/3584372.3588673 · Cited by 12

Locally Private Set-Valued Data Analyses: Distribution and Heavy Hitters Estimation

Wang, S., Li, Y., Zhong, Y., Chen, K., Wang, X., Zhou, Z., Peng, F., Qian, Y., Du, J., & Yang, W. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3342056 · Cited by 12

Efficient and Privacy-Enhanced Federated Learning Based on Parameter Degradation

Li, W., Yu, P., Cheng, Y., Yan, J., & Zhang, Z. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3399659 · Cited by 12

Securing Personally Identifiable Information: A Survey of SOTA Techniques, and a Way Forward

Makhdoom, I., Abolhasan, M., Lipman, J., Shariati, N., Franklin, D. R., & Piccardi, M. (2024) · IEEE Access · doi:10.1109/access.2024.3447017 · Cited by 12

Generating Location Traces With Semantic- Constrained Local Differential Privacy

Sun, X., Ye, Q., Hu, H., Duan, J., Xue, Q., Wo, T., Zhang, W., & Xu, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3480712 · Cited by 12

A Local Differential Privacy Hybrid Data Clustering Iterative Algorithm for Edge Computing

Zhou, Y., Wang, Z., & Liu, Y. (2024) · Chinese Journal of Electronics · doi:10.23919/cje.2023.00.332 · Cited by 12

Differential privacy in the wild

Machanavajjhala, A., He, X., & Hay, M. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/3007263.3007322 · Cited by 12

SCRAPPOR: An Efficient Privacy-Preserving Algorithm Base on Sparse Coding for Information-Centric IoT

Cao, H., Liu, S., Wu, L., & Guan, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2876707 · Cited by 12

Confidential Boosting with Random Linear Classifiers for Outsourced User-Generated Data

Sharma, S., & Chen, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29959-0_3 · Cited by 13

Inferring ground truth from crowdsourced data under local attribute differential privacy

Wang, D., & Xu, J. (2021) · Theoretical Computer Science · doi:10.1016/j.tcs.2021.02.039 · Cited by 12

Privacy protection in government data sharing: an improved LDP-based approach

Piao, C., Hao, Y., Yan, J., & Jiang, X. (2021) · Service Oriented Computing and Applications · doi:10.1007/s11761-021-00315-3 · 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

Differentially private model release for healthcare applications

Sangeetha, S., Sadasivam, G. S., & Srikanth, A. (2022) · International Journal of Computers and Applications · doi:10.1080/1206212x.2021.2024958 · 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

Private Lives Matter

Bakas, A., Michalas, A., & Dimitriou, T. D. (2022) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3508398.3511514 · Cited by 12

In Search of Lost Utility: Private Location Data

Lestyán, S., Ács, G., & Biczók, G. (2022) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2022-0076 · Cited by 12

Strengthening Order Preserving Encryption with Differential Privacy

Chowdhury, A. R., Ding, B., Jha, S., Liu, W., & Zhou, J. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560610 · Cited by 14

Frequency Estimation Mechanisms Under ϵδ-Utility-Optimized Local Differential Privacy

Zhang, Y., Zhu, Y., Zhou, Y., & Yuan, J. (2023) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3238839 · Cited by 12

Investigating the Visual Utility of Differentially Private Scatterplots

Panavas, L., Crnovrsanin, T., Adams, J. L., Ullman, J., Sarvghad, A., Tory, M. K., & Dunne, C. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3292391 · Cited by 12

Private Convex Optimization via Exponential Mechanism

Gopi, S., Lee, Y. T., & Liu, D. (2024) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.869 · Cited by 12

Privacy preserving big histogram aggregation for spatial crowdsensing

Wang, S., Huang, L., Wang, P., Shen, Y., Xu, H., & Yang, W. (2015) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/pccc.2015.7410339 · Cited by 12

Addressing Data-Centric Security Requirements for IoT-Based Systems

Rodriguez, J. D. P., Schreckling, D., & Posegga, J. (2016) · International Workshop Secure Internet Things · doi:10.1109/siot.2016.007 · Cited by 12

Privacy-preserved distinct content collection in human-assisted ubiquitous computing systems

Zheng, X., Chen, A., Luo, G., Tian, L., & Cai, Z. (2019) · Information Sciences · doi:10.1016/j.ins.2019.04.036 · Cited by 12

Differential Privacy-preserving Distributed Machine Learning

Wang, X., Ishii, H., Du, L., Cheng, P., & Chen, J. (2019) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc40024.2019.9029938 · Cited by 12

LightDP: towards automating differential privacy proofs

Zhang, D., & Kifer, D. (2017) · ACM SIGPLAN Notices · doi:10.1145/3093333.3009884 · Cited by 12

Linear and Range Counting under Metric-based Local Differential Privacy

Xiang, Z., Ding, B., He, X., & Zhou, J. (2020) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit44484.2020.9173952 · Cited by 12

DDUO: General-Purpose Dynamic Analysis for Differential Privacy

Abuah, C., Silence, A., Darais, D., & Near, J. P. (2021) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf51468.2021.00043 · Cited by 12

A Theory of PAC Learnability of Partial Concept Classes

Alon, N., Hanneke, S., Holzman, R., & Moran, S. (2022) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs52979.2021.00070 · Cited by 34

Vehicle-based secure location clustering for IoT-equipped building and facility management in smart city

Wu, H., Li, L., Liu, Y., & Wu, X. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.108937 · Cited by 12

Privately Learning Markov Random FieldsPreprint

Zhang, H., Kamath, G., Kulkarni, J., & Wu, Z. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09463 · Cited by 12

On the Sample Complexity of Privately Learning Unbounded High-Dimensional GaussiansPreprint

Aden-Ali, I., Ashtiani, H., & Kamath, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09929 · Cited by 12

The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower BoundsPreprint

Cai, T., Wang, Y., & Zhang, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03900 · Cited by 12

Information-Theoretic Approaches to Differential Privacy

Ünsal, A., & Önen, M. (2023) · ACM Computing Surveys · doi:10.1145/3604904 · Cited by 16

An LDP-Based Privacy-Preserving Longitudinal and Multidimensional Range Query Scheme in IoT

Ni, Y., Li, J., Chang, W., & Xiao, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3306003 · 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

Privacy-Preserving Data Selection for Horizontal and Vertical Federated Learning

Zhang, L., Li, A., Peng, H., Han, F., Huang, F., & Li, X. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3439709 · Cited by 13

Robust Federated Learning for Privacy Preservation and Efficiency in Edge Computing

Zhou, H., Dai, H., Yang, G., & Xiang, Y. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3562359 · Cited by 11

Personalized Local Differential Privacy for Multi-Dimensional Range Queries Over Mobile User Data

He, Y., Wang, M., Deng, X., Yang, P., Xue, Q., & Yang, L. T. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3568511 · Cited by 11

An Attack-Agnostic Defense Framework Against Manipulation Attacks Under Local Differential Privacy

Zhao, P., Zhang, Z., Dong, J., Wu, J., Liu, Z., Wang, S., & Gao, Y. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00255 · Cited by 11

Memory Optimization for Energy-Efficient Differentially Private Deep Learning

Edstrom, J., Das, H., Xu, Y., & Gong, N. (2019) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2019.2946128 · Cited by 11

Measuring Membership Privacy on Aggregate Location Time-Series

Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2020) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/3393691.3394200 · Cited by 12

The Value of Privacy

Wang, W., Ying, L., & Zhang, J. (2016) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/2964791.2901461 · Cited by 11

When Deep Learning Meets Steganography: Protecting Inference Privacy in the Dark

Liu, Q., Yang, J., Jiang, H., Wu, J., Peng, T., Wang, T., & Wang, G. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796975 · Cited by 11

Collecting Individual Trajectories under Local Differential Privacy

Yang, J., Cheng, X., Su, S., Sun, H., & Chen, C. (2022) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm55031.2022.00035 · Cited by 11

Bayes Security: A Not So Average Metric

Konstantinos Chatzikokolakis, A., Cherubin, G., Catuscia Palamidessi, A., & Troncoso, C. (2023) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf57540.2023.00011 · Cited by 16

Sampling and partitioning for differential privacy

Ebadi, H., Antignac, T., & Sands, D. (2016) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2016.7906954 · Cited by 11

LinkedIn Salary: A System for Secure Collection and Presentation of Structured Compensation Insights to Job Seekers

Kenthapadi, K., Chudhary, A., & Ambler, S. (2017) · IEEE Symposium on Privacy-Aware Computing (PAC) · doi:10.1109/pac.2017.9 · Cited by 11

Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network

Ju, C., Gu, Q., Wu, G., & Zhang, S. (2020) · Sensors · doi:10.3390/s20092516 · Cited by 11

Top- k closed co-occurrence patterns mining with differential privacy over multiple streams

Wang, J., Fang, S., Liu, C., Qin, J., Li, X., & Shi, Z. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.04.049 · Cited by 11

On the Privacy and Utility Properties of Triple Matrix-Masking

Ding, A. A., Miao, G., & Wu, S. S. (2020) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.674 · Cited by 11

Survey of recommender systems based on federated learning

梁, 锋., 羊, 恩., Pan, W., 杨, 强., & 明, 仲. (2021) · Scientia Sinica Informationis · doi:10.1360/ssi-2021-0329 · Cited by 11

Asymmetric Differential Privacy

Takagi, S., Kato, F., Cao, Y., & Yoshikawa, M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020709 · Cited by 11

DPCrowd: Privacy-Preserving and Communication-Efficient Decentralized Statistical Estimation for Real-Time Crowdsourced Data

Ren, X., Yu, C.-M., Yu, W., Yang, X., Zhao, J., & Yang, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3020089 · Cited by 12

Locally Differentially Private Naive Bayes ClassificationPreprint

Yılmaz, E., Al-Rubaie, M., & Chang, J. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01039 · Cited by 11

Federated Learning in Adversarial SettingsPreprint

Kerkouche, R., Ács, G., & Castelluccia, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07808 · Cited by 11

Revisiting the Privacy Needs of Real-World Applicable Company BenchmarkingPreprint

Pennekamp, J., Sapel, P., Fink, I. B., Wagner, S., Reuter, S., Hopmann, C., Wehrle, K., & Henze, M. (2020) · IACR Cryptology ePrint Archive · doi:10.18154/rwth-2021-01321 · 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

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

Mining frequent items from high-dimensional set-valued data under local differential privacy protection

Wu, H., Ran, R., Peng, S., Yang, M., & Guo, T. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121105 · Cited by 10

SPARR: Spintronics-based private aggregatable randomized response for crowdsourced data collection and analysis

Tsou, Y.-T., Zhen, H., Kuo, S., Chang, C., Fukushima, A., & Rong, B.-D. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.01.001 · Cited by 5

A Practical Privacy Protection Procedure for Gathering Categorical Data

Nayak, T. K., & Zhai, X. (2025) · Sankhya B · doi:10.1007/s13571-025-00382-y

Collection and Analysis of Sensitive Data with Privacy Protection by a Distributed Randomized Response Protocol

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