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

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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

Warner, S. L. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480775 · Cited by 3,261

Network Applications of Bloom Filters: A Survey

Broder, A., & Mitzenmacher, M. (2004) · Internet Mathematics · doi:10.1080/15427951.2004.10129096 · Cited by 2,003

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

INSPECTRE: Privately Estimating the Unseen

Acharya, J., Kamath, G., Sun, Z., & Zhang, H. (2020) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.724 · Cited by 19

Multi-Dimensional Randomized Response

Domingo‐Ferrer, J., & Soria-Comas, J. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3045759 · Cited by 22

Sarve: synthetic data and local differential privacy for private frequency estimation

Varma, G., Chauhan, R., & Singh, D. (2022) · Cybersecurity · doi:10.1186/s42400-022-00129-6 · Cited by 18

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

Ren, J., Jiang, L., Peng, H. M., Lyu, L., Liu, Z., Chen, C., Wu, J. X., Bai, X., & Yu, P. S. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557278 · Cited by 19

Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems

Nuradha, T., Goldfeld, Z., & Wilde, M. M. (2024) · IEEE Transactions on Information Theory · doi:10.1109/tit.2024.3404927 · Cited by 32

Privacy-preserving generation and publication of synthetic trajectory microdata: A comprehensive survey

Kim, J. W., & Jang, B. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103951 · Cited by 20

Profile-based Privacy for Locally Private Computations

Geumlek, J., & Chaudhuri, K. (2019) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2019.8849549 · Cited by 18

Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning

Wang, L., & Gu, Q. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/519 · Cited by 18

Investigating Visual Analysis of Differentially Private Data

Zhang, D., Sarvghad, A., & Miklau, G. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030369 · Cited by 18

A User-Centric Mechanism for Sequentially Releasing Graph Datasets under Blowfish Privacy

Chicha, E., Bouna, B. A., Nassar, M., Chbeir, R., Haraty, R. A., Oussalah, M., Benslimane, D., & Alraja, M. N. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3431501 · Cited by 18

Privacy-preserving collaborative machine learning in biomedical applications

Kim, W., & Seok, J. (2022) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic54071.2022.9722703 · Cited by 18

Smart IoT Data Collection

Fotiou, N., Siris, V. A., Mertzianis, A., & Polyzos, G. C. (2018) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2018.8449766 · Cited by 18

Balancing Unobserved Covariates With Covariate-Adaptive Randomized Experiments

Liu, Y., & Hu, F. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1825450 · Cited by 18

Understanding Unintended Memorization in Federated LearningPreprint

Thakkar, O. D., Ramaswamy, S., Mathews, R., & Beaufays, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07490 · Cited by 19

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

A Survey of Data Pricing MethodsPreprint

Zhang, M., & Beltrán, F. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3609120 · Cited by 18

Communication-Efficient Triangle Counting under Local Differential PrivacyPreprint

Imola, J., Murakami, T., & Chaudhuri, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06485 · Cited by 18

Confidential machine learning on untrusted platforms: a survey

Sagar, S., & Chen, K. (2021) · Cybersecurity · doi:10.1186/s42400-021-00092-8 · Cited by 21

InaudibleKey

Xu, W., Li, Z., Xue, W., Yu, X., Wei, B. W., Wang, J., Luo, C., Li, W., & Zomaya, A. Y. (2021) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1145/3412382.3458260 · Cited by 17

SparkAC: Fine-Grained Access Control in Spark for Secure Data Sharing and Analytics

Xue, T., Wen, Y., Luo, B., Li, G., Li, Y., Zhang, B., Zheng, Y., Hu, Y., & Meng, D. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3149544 · Cited by 16

Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Tasks

Li, J., Liao, G., Chen, L., & Chen, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3312304 · Cited by 17

Locally Differentially Private Document Generation Using Zero Shot Prompting

Utpala, S., Hooker, S., & Chen, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.566 · Cited by 51

Privacy-Preserving Location-Based Advertising via Longitudinal Geo-Indistinguishability

Yu, L., Zhang, S., Meng, Y., Du, S., Chen, Y., Ren, Y., & Zhu, H. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3348136 · Cited by 17

Differentially Private Algorithms for Learning Mixtures of Separated Gaussians

Kamath, G., Sheffet, O., Singhal, V., & Ullman, J. (2020) · Information Theory and Applications Workshop (ITA) · doi:10.1109/ita50056.2020.9244945 · Cited by 18

Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms

Asi, H., & Duchi, J. C. (2020) · neural information processing systems · Cited by 17

PrivBV: Distance-aware encoding for distributed data with local differential privacy

Sun, L., Ping, G., & Ye, X. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2021.9010027 · Cited by 17

Data Confidentiality and Information Credibility in On-line Ecosystems

Livraga, G., & Viviani, M. (2019) · International Conference on Management of Emergent Digital EcoSystems (MEDES) · doi:10.1145/3297662.3365829 · Cited by 18

Differentially Private Hypothesis Testing, Revisited.Preprint

Wang, Y., Lee, J. W., & Kifer, D. (2015) · arXiv (Cornell University) · Cited by 19

LDPMF: Local differential privacy enhanced matrix factorization for advanced recommendation

Li, X., Zhou, W., Haq, A. U., & Khan, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112892 · Cited by 16

A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues

Wang, Z., Ji, H., Zhu, Y., Wang, D., & Han, Z. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3558755 · Cited by 18

Local Information Privacy with Bounded Prior

Jiang, B., Li, M., & Tandon, R. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761176 · Cited by 16

R2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions

Mohammady, M., Xie, S., Hong, Y., Zhang, M., Wang, L., Pourzandi, M., & Debbabi, M. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417259 · Cited by 16

Inference Under Information Constraints III: Local Privacy Constraints

Acharya, J., Canonne, C. L., Freitag, C., Sun, Z., & Tyagi, H. (2021) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2021.3053569 · Cited by 18

Statistical Quantification of Differential Privacy: A Local Approach

Askin, Ö., Kutta, T., & Dette, H. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833689 · Cited by 16

Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

Yan, Y., Sun, Z., Mahmood, A., Xu, F., Dong, Z., & Sheng, Q. Z. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070404 · Cited by 16

PRISM: Prefix-Sum based Range Queries Processing Method under Local Differential Privacy

Wang, Y., & Cheng, X. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00037 · Cited by 16

Locally differentially private high-dimensional data synthesis

Chen, X., Wang, C., Yang, Q., Hu, T., & Jiang, C. (2022) · Science China Information Sciences · doi:10.1007/s11432-022-3583-x · Cited by 16

PPDU: dynamic graph publication with local differential privacy

Hou, L., Ni, W., Zhang, S., Fu, N., & Zhang, D. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01838-1 · Cited by 16

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

Characterization and Quantification of User Privacy: Key Challenges, Regulations, and Future Directions

Arshad, R., & Asghar, M. R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3519861 · Cited by 19

Buying Data from Privacy-Aware Individuals: The Effect of Negative Payments

Wang, W., Ying, L., & Zhang, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-54110-4_7 · Cited by 16

Collecting Preference Rankings Under Local Differential Privacy

Yang, J., Cheng, X., Su, S., Chen, R., Ren, Q., & Liu, Y. (2019) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2019.00151 · Cited by 16

An Efficient Privacy-Preserving Algorithm Based on Randomized Response in IoT-Based Smart Grid

Cao, H., Liu, S., Guan, Z., Wu, L., Deng, H., & Du, X. (2018) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld.2018.00160 · Cited by 16

Exponential Separations in Local Differential Privacy

Joseph, M., Mao, J., & Roth, A. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975994.31 · Cited by 16

Threats Faced by Mixed Reality and Countermeasures

Syal, S., & Mathew, R. R. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.04.295 · Cited by 16

Measuring Membership Privacy on Aggregate Location Time-Series

Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2020) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3392154 · Cited by 19

An Overview of De-Identification Techniques and Their Standardization Directions

Youm, H. Y. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019ici0002 · Cited by 17

Secure Sublinear Time Differentially Private Median Computation

Böhler, J., & Kerschbaum, F. (2020) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.24150 · Cited by 16

Privacy-Preserving Estimation of $k$ -Persistent Traffic in Vehicular Cyber-Physical Systems

Sun, Y.-E., Huang, H., Chen, S., Zhou, Y., Han, K., & Yang, W. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2916349 · Cited by 16

DPSAaS

Xu, M., Wang, T., Ding, B., Zhou, J., Hong, C., & Huang, Z. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3352063.3352085 · Cited by 16

Differentially Private Imaging via Latent Space ManipulationPreprint

Li, T., & Clifton, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.05472 · Cited by 16

Privacy Preserving Time-Series Forecasting of User Health Data Streams

Imtiaz, S., Horchidan, S.-F., Abbas, Z., Arsalan, M., Chaudhry, H. N., & Vlassov, V. (2020) · 2020 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata50022.2020.9378186 · Cited by 15

Federated Learning Framework Based on Distributed Storage and Diffusion Model for Intrusion Detection on IoT Networks

Ricardo Manzano, S., Zaman, M., Upadhyay, D., Goel, N., & Sampalli, S. (2025) · IEEE Access · doi:10.1109/access.2025.3565409 · Cited by 18

Data-Driven Caching With Users’ Content Preference Privacy in Information-Centric Networks

Zhang, X., Li, H., Wang, J., Guo, Y., Pei, Q., Li, P., & Pan, M. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3069763 · Cited by 15

Offset-Symmetric Gaussians for Differential Privacy

Sadeghi, P., & Korki, M. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3185770 · Cited by 15

Analyzing Preference Data With Local Privacy: Optimal Utility and Enhanced Robustness

Wang, S., Luo, X., Qian, Y., Du, J., Lin, W., & Yang, W. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3207486 · Cited by 15

GC-NLDP: A graph clustering algorithm with local differential privacy

Fu, N., Ni, W., Zhang, S., Hou, L., & Zhang, D. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102967 · Cited by 15

STAR

Davidson, A., Snyder, P., Quirk, E. B., Genereux, J., Livshits, B., & Haddadi, H. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560631 · Cited by 15

A Privacy-Preserving State Estimation Scheme for Smart Grids

Tran, H.-Y., Hu, J., & Pota, H. R. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3210017 · Cited by 16

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

Efficient Defenses Against Output Poisoning Attacks on Local Differential Privacy

Song, S., Xu, L., & Zhu, L. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3305873 · Cited by 15

Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas

Wang, T., Zhao, J., Yu, H., Liu, J., Yang, X., Ren, X., & Shi, S. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357954 · Cited by 15

Principal Component Analysis in the Local Differential Privacy Model

Wang, D., & Xu, J. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/666 · Cited by 15

A workload-adaptive mechanism for linear queries under local differential privacy

McKenna, R., Maity, R. K., Mazumdar, A., & Miklau, G. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3407790.3407798 · Cited by 15

MPLDS: An integration of CP-ABE and local differential privacy for achieving multiple privacy levels data sharing

Song, H., Han, X., Lv, J., Luo, T., & Li, J. (2021) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01238-8 · Cited by 15

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Sei, Y., & Ohsuga, A. (2022) · IEEE Access · doi:10.1109/access.2022.3143813 · Cited by 15

Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python

Arcolezi, H. H., Couchot, J.-F., Gambs, S., Catuscia Palamidessi, A., & Zolfaghari, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17143-7_40 · Cited by 15

Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It

Casacuberta, S., Shoemate, M., Vadhan, S., & Wagaman, C. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560708 · Cited by 15

Differential Privacy Preserving Causal Graph Discovery

Xu, D., Yuan, S., & Wu, X. (2017) · IEEE Symposium on Privacy-Aware Computing (PAC) · doi:10.1109/pac.2017.24 · Cited by 15

RoD: Evaluating the Risk of Data Disclosure Using Noise Estimation for Differential Privacy

Tsou, Y.-T., Chen, H.-L., & Chen, J.-Y. (2019) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2916108 · Cited by 15

An Efficient Data Aggregation Scheme with Local Differential Privacy in Smart Grid

Gai, N., Xue, K., He, P., Zhu, B., Liu, J., & He, D. (2020) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn50589.2020.00027 · Cited by 15

Private Identity Testing for High-Dimensional DistributionsPreprint

Canonne, C. L., Kamath, G., McMillan, A., Ullman, J., & Zakynthinou, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11947 · Cited by 24

Locally Private Hypothesis SelectionPreprint

Gopi, S., Kamath, G., Kulkarni, J., Nikolov, A., Wu, Z. S., & Zhang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09465 · Cited by 15

Differentially Private Assouad, Fano, and Le CamPreprint

Acharya, J., Sun, Z., & Zhang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.06830 · Cited by 16

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

Privacy-Preserving Offloading in Edge Intelligence Systems With Inductive Learning and Local Differential Privacy

Tchaye-Kondi, J., Zhai, Y., Shen, J., & Zhu, L. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3266257 · Cited by 14

Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices

Pan, Q., Zhu, Y., & Chu, L. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00149 · Cited by 14

Community detection in decentralized social networks with local differential privacy

Fu, N., Ni, W., Hou, L., Zhang, D., & Zhang, R. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120164 · Cited by 14

Pervasive User Data Collection from Cyberspace: Privacy Concerns and Countermeasures

Jiang, Y., Baee, M. A. R., Simpson, L. R., Gauravaram, P., Pieprzyk, J., Zia, T. A., Zhao, Z., & Le, Z. (2024) · Cryptography · doi:10.3390/cryptography8010005 · Cited by 14

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

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

Li, X., Liu, W., Lou, J., Hong, Y., Zhang, L., Qin, Z., & Ren, K. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3639285 · Cited by 14

HierFedPDP:Hierarchical federated learning with personalized differential privacy

Li, S., Liu, Y., Feng, F., Liu, Y., Li, X., & Liu, Z. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103890 · Cited by 14

Federated Learning Meets Urban Opportunistic Crowdsensing in 6G Networks: Opportunities, Challenges, and Optimization Potentials

Zhang, W., Liu, X., Zhu, C., Varjonen, S., Wang, F., & Tarkoma, S. (2024) · IEEE Network · doi:10.1109/mnet.2024.3520552 · Cited by 14

High-Dimensional Crowdsourced Data Distribution Estimation with Local Privacy

Ren, X., Yu, C.-M., Yu, W., Yang, S., Yang, X., & McCann, J. A. (2016) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2016.57 · Cited by 14

LanguageLogger: A Mobile Keyboard Application for Studying Language Use in Everyday Text Communication in the Wild

Bemmann, F., & Buschek, D. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3397872 · Cited by 18

Orchard: Differentially Private Analytics at Scale

Roth, E., Zhang, H., Haeberlen, A., & Pierce, B. C. (2020) · Operating Systems Design and Implementation · Cited by 14

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

An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

Xu, C., Mei, X., Liu, D., Zhao, K., & Ding, A. S. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00917-0 · Cited by 14

Fair NLP Models with Differentially Private Text Encoders

Maheshwari, G., Denis, P., Mikaela Keller, A., & Bellet, A. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.514 · Cited by 15

A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy

Ahuja, R., Zeighami, S., Ghinita, G., & Shahabi, C. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588701 · Cited by 16

Using Noisy Binary Search for Differentially Private Anomaly Detection

Bittner, D. M., Sarwate, A. D., & Wright, R. N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94147-9_3 · Cited by 14

Locally private Jaccard similarity estimation

Yan, Z., Wu, Q., Ren, M., Liu, J., Liu, S., & Qiu, S. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4889 · Cited by 14

Towards Instance-Optimal Private Query Release

Błasiok, J., Bun, M., Nikolov, A., & Steinke, T. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975482.152 · Cited by 14

Metadata-private communication for the 99%

Gilad, Y. (2019) · Communications of the ACM · doi:10.1145/3338537 · Cited by 14

RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything

Li, Z., Tian, Y., Zhang, W., Liao, Q., Liu, Y., Du, X., & Guizani, M. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3059958 · Cited by 14

Differential Privacy Preservation in Interpretable Feedforward-Designed Convolutional Neural Networks

Li, D., Wang, J., Zhou, T., Li, X., & Hu, Y. (2020) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom50675.2020.00089 · Cited by 14

PrivacyProxy: Leveraging Crowdsourcing and In Situ Traffic Analysis to Detect and Mitigate Information LeakagePreprint

Srivastava, G., Bhuwalka, K., Sahoo, S. K., Chitkara, S., Ku, K., Fredrikson, M., Hong, J., & Agarwal, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.06384 · Cited by 14

SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text MiningPreprint

Weggenmann, B., & Kerschbaum, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.00904 · Cited by 14

Assessing differentially private deep learning with Membership InferencePreprint

Bernau, D., Grassal, P.-W., Robl, J., & Kerschbaum, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11328 · Cited by 30

A Review of Privacy Preserving Federated Learning for Private IoT Analytics.Preprint

Briggs, C. C., Fan, Z., & András, P. (2020) · arXiv (Cornell University) · Cited by 15

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

Imran, F., & Meo, R. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3636024 · Cited by 1

PPDCA: Privacy-preserving crowdsensing data collection and analysis with randomized response

Lin, B., Wu, S.-H., Tsou, Y.-T., & Huang, Y. (2018) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2018.8377050 · Cited by 8

Privacy Parameter Variation Using RAPPOR on a Malware Dataset

Aaby, P., Acuna, J. M. D., Macfarlane, R., & Buchanan, W. J. (2018) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom/bigdatase.2018.00133 · Cited by 3