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

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

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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

REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service

Qu, W., Jia, J., & Gong, N. Z. (2023) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2023.24444 · Cited by 6

Two Birds With One Stone: Differential Privacy by Low-Power SRAM Memory

Liu, J., Gong, N., & Das, H. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3382630 · Cited by 6

Differentially Private Fair Binary Classifications

Ghoukasian, H., & Asoodeh, S. (2024) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit57864.2024.10619147 · Cited by 6

Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential Privacy

Chen, W., Chen, H., Han, T., Tong, W., & Zhong, S. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3464744 · Cited by 6

SUHDSA: Secure, Useful, and High-Performance Data Stream Anonymization

Joo, Y., & Kim, S. S. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3476684 · Cited by 6

SPAS: Continuous Release of Data Streams under w-Event Differential Privacy

Li, X., Li, T., Cheng, Y., Gong, C., Ren, K., Qin, Z., & Wang, T. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3714420 · Cited by 6

Introducing Privacy in Screen Event Frequency Analysis for Android Apps

Zhang, H., Latif, S., Bassily, R., & Rountev, A. (2019) · IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM) · doi:10.1109/scam.2019.00037 · Cited by 6

Methods and Approaches for Privacy-Preserving Machine Learning

Lisin, N., & Zapechnikov, S. V. (2020) · Mechanisms and machine science · doi:10.1007/978-3-030-33491-8_17 · Cited by 6

Generalized Iterative Bayesian Update and Applications to Mechanisms for Privacy Protection

ElSalamouny, E., & Catuscia Palamidessi, A. (2020) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp48549.2020.00038 · Cited by 6

Preserving Genomic Privacy via Selective Sharing

Yılmaz, E., Ayday, E., Ji, T., & Li, P. (2020) · Workshop on Privacy in the Electronic Society · doi:10.1145/3411497.3420214 · Cited by 6

Differential Privacy for Binary Functions via Randomized Graph Colorings

D’Oliveira, R. G. L., Médard, M., & Sadeghi, P. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9517935 · Cited by 6

Secure k-Anonymization over Encrypted Databases

Kesarwani, M., Kaul, A., Braghin, S., Holohan, N., & Antonatos, S. (2021) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud53861.2021.00015 · Cited by 6

Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment

Kim, J. W. (2021) · Applied Sciences · doi:10.3390/app112210740 · Cited by 6

Android Web Security Solution using Cross-device Federated Learning

Singh, A. K., & Goyal, N. (2022) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets53615.2022.9668449 · Cited by 6

Preserving Privacy for Distributed Genome-Wide Analysis Against Identity Tracing Attacks

Zhang, Y., Bai, G., Li, X., Nepal‬, ‪., Grobler, M. M., Chen, C., & Ko, R. K. L. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3186672 · Cited by 7

Key-Value Data Collection with Distribution Estimation under Local Differential Privacy

Li, X., Yan, H., Zheng, G., Li, H., & Li, F. (2022) · Security and Communication Networks · doi:10.1155/2022/1330251 · Cited by 6

Event-set differential privacy for fine-grained data privacy protection

Wang, T., Yang, W., Ma, X., & Wang, B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.10.006 · Cited by 6

DP-Opt: Identify High Differential Privacy Violation by Optimization

Niu, B., Zhou, Z., Chen, Y., Cao, J. P., & Li, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19214-2_34 · Cited by 6

Examining the Utility of Differentially Private Synthetic Data Generated using Variational Autoencoder with TensorFlow Privacy

Tai, B.-C., Li, S.-C., Huang, Y., & Wang, P.-C. (2022) · IEEE Pacific Rim International Symposium on Dependable Computing (PRDC) · doi:10.1109/prdc55274.2022.00038 · Cited by 6

Towards Measuring Fairness for Local Differential Privacy

Salas, J., Torra, V., & Megías, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25734-6_2 · Cited by 6

Towards Defending Against Byzantine LDP Amplified Gain Attacks

Yan, Y., Ye, Q., Hu, H., Chen, R., Han, Q., & Wang, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30637-2_42 · Cited by 6

The promise and limitations of formal privacy

Williams, A. R., & Bowen, C. M. (2023) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1615 · Cited by 6

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

Differentially Private Heatmaps

Ghazi, B., He, J., Kohlhoff, K., Kumar, R., Manurangsi, P., Navalpakkam, V., & Valliappan, N. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i6.25933 · Cited by 6

Differentially Private Fair Division

Manurangsi, P., & Suksompong, W. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25721 · Cited by 6

DPAUC: Differentially Private AUC Computation in Federated Learning

Sun, J., Yang, X., Yao, Y., Xie, J., Wu, D., & Wang, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26770 · Cited by 6

Flag: A Framework for Lightweight Robust Secure Aggregation

Bangalore, L., Sereshgi, M. H. F., Hazay, C., & Venkitasubramaniam, M. (2023) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3579856.3595805 · Cited by 8

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

Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy Preserving Quantum Machine Learning

Watkins, W., Wang, H., Bae, S., Tseng, H.-H., Cha, J., Chen, S. Y.-C., & Yoo, S. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447786 · Cited by 6

LDP-Purifier: Defending against Poisoning Attacks in Local Differential Privacy

Wang, L., Ye, Q., Hu, H., Meng, X., & Huang, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5562-2_14 · Cited by 6

Implementing Informed Consent as Information-Flow Policies for Secure Analytics on eHealth Data: Principles and Practices

Gjerdrum, A. T., Johansen, H. D., & Johansen, D. (2016) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase.2016.39 · Cited by 6

ConTPL

Cao, Y., Xiong, L., Yoshikawa, M., Xiao, Y., & Zhang, S. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3229863.3236267 · Cited by 6

Geometric Noise for Locally Private Counting Queries

Kacem, L., & Catuscia Palamidessi, A. (2018) · ACM Workshop on Programming Languages and Analysis for Security · doi:10.1145/3264820.3264827 · Cited by 6

On-Device Algorithms for Public-Private Data with Absolute Privacy

Epasto, A., Esfandiari, H., & Mirrokni, V. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313677 · Cited by 6

RRTxFM: Probabilistic Counting for Differentially Private Statistics

von Voigt, S. N., & Tschorsch, F. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-39634-3_9 · Cited by 6

Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation

Wang, D., & Xu, J. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/665 · Cited by 6

PSynDB

Huang, Z., McKenna, R., Bissias, G. D., Miklau, G., Hay, M., & Machanavajjhala, A. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3352063.3352099 · Cited by 6

A logical characterization of differential privacy

Castiglioni, V., Konstantinos Chatzikokolakis, A., & Catuscia Palamidessi, A. (2020) · Science of Computer Programming · doi:10.1016/j.scico.2019.102388 · Cited by 6

A firm foundation for statistical disclosure control

Hoshino, N. (2020) · Japanese Journal of Statistics and Data Science · doi:10.1007/s42081-020-00086-9 · Cited by 7

Differentially Private Machine Learning Model against Model Extraction Attack

Cheng, Z., Li, Z., Zhang, J., & Zhang, S. (2020) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00125 · Cited by 6

Personalized Data Collection Based on Local Differential Privacy in the Mobile Crowdsensing

Li, F., Song, H., & Li, J. (2020) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc51575.2020.9345209 · Cited by 6

Investigating Local Differential Privacy and Generative Adversarial Network in Collecting Data

Ha, T., & Dano, T. K. (2020) · International Conference on Advanced Computing and Applications (ACOMP) · doi:10.1109/acomp50827.2020.00028 · Cited by 6

Differential Privacy-Preserving User Linkage across Online Social Networks

Yao, X., Zhang, R., & Zhang, Y. (2021) · International Workshop on Quality of Service · doi:10.1109/iwqos52092.2021.9521333 · Cited by 6

Towards Private Medical Data Donations by Using Privacy Preserving Technologies

Appenzeller, A., Terzer, N., Krempel, E., & Beyerer, J. (2022) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3529190.3534768 · Cited by 6

Differentially Private Graph Publishing Through Noise-Graph Addition

Salas, J., González-Zelaya, V., Torra, V., & Megías, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33498-6_18 · Cited by 6

Threat Modeling of Connected Vehicles: A privacy analysis and extension of vehicleLang

Xiong, W., & Lagerström, R. (2019) · International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) · doi:10.1109/cybersa.2019.8899615 · Cited by 6

Local Differential Privacy with K-anonymous for Frequency Estimation

Zhao, D., Chen, H., Zhao, S., Zhang, X., Li, C., & Liu, R. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006022 · Cited by 6

BUDS: Balancing Utility and Differential Privacy by Shuffling

Sengupta, P., Paul, S., & Mishra, S. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225470 · Cited by 6

Constrained Private Mechanisms for Count Data

Cormode, G., Kulkarni, T., & Srivastava, D. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2912179 · Cited by 6

A General Characterization of the Statistical Query ComplexityPreprint

Feldman, V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.02198 · Cited by 8

Topology of Privacy: Lattice Structures and Information Bubbles for Inference and ObfuscationPreprint

Erdmann, M. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.04130 · Cited by 6

Constrained Differential Privacy for Count DataPreprint

Cormode, G., Kulkarni, T., & Srivastava, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.00608 · Cited by 6

The Podium Mechanism: Improving on the Laplace and Staircase MechanismsPreprint

Pihur, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00191 · Cited by 6

Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. IndistinguishabilityPreprint

Wang, S., Du, J., Yang, W., Diao, X., Liu, Z., Nie, Y., Huang, L., & Xu, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.04920 · Cited by 6

Crypt$ε$: Crypto-Assisted Differential Privacy on Untrusted ServersPreprint

Chowdhury, A. R., Wang, C., He, X., Machanavajjhala, A., & Jha, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07756 · Cited by 12

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

Privacy-Preserving Image Classification in the Local SettingPreprint

Wang, S., & Chang, J. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03261 · Cited by 6

DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging ApplicationsPreprint

Wagh, S., He, X., Machanavajjhala, A., & Mittal, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.08887 · Cited by 6

Revisiting Model-Agnostic Private Learning: Faster Rates and Active LearningPreprint

Liu, C., Zhu, Y., Chaudhuri, K., & Wang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03186 · Cited by 6

Decision Making with Differential Privacy under a Fairness LensPreprint

Fioretto, F., Tran, C., & Hentenryck, P. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.07513 · Cited by 6

Privacy Preserving Data FusionPreprint

Tian, L., Turjeman, D., & Levy, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4451656 · Cited by 6

Information-Theoretic Fairness with a Bounded Statistical Parity Constraint

Zamani, A., Changizi, A., Thobaben, R., & Skoglund, M. (2025) · International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) · doi:10.23919/wiopt66569.2025.11123370 · Cited by 5

Dynamic differential privacy technique for deep learning models

Elabd, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-27708-0 · Cited by 6

A Tighter Converse for the Locally Differentially Private Discrete Distribution Estimation Under the One-bit Communication Constraint

Nam, S.-H., & Lee, S.-H. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3205276 · Cited by 5

Sensitive Data Privacy Protection of Carrier in Intelligent Logistics System

Yao, Z., Tan, L., Yi, J., Fu, L., Zhang, Z., Tan, X., Xie, J., She, K., Yang, P., Wu, W., et al. (2024) · Symmetry · doi:10.3390/sym16010068 · Cited by 5

$\mathsf {AVeCQ}$: Anonymous Verifiable Crowdsourcing With Worker Qualities

Koutsos, V., Damle, S., Papadopoulos, D., Gujar, S., & Chatzopoulos, D. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3396342 · Cited by 5

InaudibleKey2.0: Deep Learning-Empowered Mobile Device Pairing Protocol Based on Inaudible Acoustic Signals

Yang, H., Li, Z., Luo, C., Wei, B. W., & Xu, W. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3407783 · Cited by 5

A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results

Makhlouf, K., Stefanović, T., Arcolezi, H. H., & Catuscia Palamidessi, A. (2024) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf61375.2024.00039 · Cited by 10

Privacy-Preserving Probabilistic Data Encoding for IoT Data Analysis

Zaman, Z., Xue, W., Gauravaram, P., Hu, W. S., Jiang, J., & Jha, S. K. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3468150 · Cited by 6

Noiseless Privacy-Preserving Decentralized Learning

Biswas, S., Even, M., Kermarrec, A.-M., Massoulié, L., Pires, R. P., Sharma, R., & de Vos, M. (2024) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2025-0043 · Cited by 11

From policy to practice in data governance and responsible data stewardship: system design for data intermediaries

Powar, J., Janssen, H., Cloete, R., & Singh, J. P. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732164 · Cited by 5

High-Dimensional Data Release With Local Differential Privacy Under IoT Architecture

Ye, X., Yang, G., Deng, H., Jie, P., Wu, R., & Jiang, H. L. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3590759 · Cited by 5

Protection method of continuous location uploading based on local differential privacy

Xiongjian, W., & Weidong, Y. (2020) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana51271.2020.00035 · Cited by 5

Random Sampling Plus Fake Data: Multidimensional Frequency Estimates With Local Differential Privacy

Arcolezi, H. H., Couchot, J.-F., Bouna, B. A., & Xiao, X. (2021) · HAL · doi:10.48550/arxiv.2109.07269 · Cited by 5

Privacy-Preserving Verifiable Asynchronous Federated Learning

Gao, Y., Wang, L., & Zhang, L. (2021) · International Conference on Software Engineering and Development (ICSED) · doi:10.1145/3507473.3507478 · Cited by 5

Local Differentially Private Fuzzy Counting in Stream Data Using Probabilistic Data Structures

Vatsalan, D., Bhaskar, R., & Kâafar, M. A. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3198478 · Cited by 5

Collecting Multidimensional Numerical Data and Estimating Mean with Personalized Local Differential Privacy

Zeng, Y., Long, S., Liu, H., & Ding, H. (2022) · Security and Communication Networks · doi:10.1155/2022/9127412 · Cited by 5

Universal optimality and robust utility bounds for metric differential privacy1

Fernandes, N., McIver, A. K., Catuscia Palamidessi, A., & Ding, M. (2023) · Journal of Computer Security · doi:10.3233/jcs-230036 · Cited by 5

Differentially Private Top-$k$ Flows Estimation Mechanism in Network Traffic

Zhu, Y., Song, Q. Y., & Luo, Y. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3309250 · Cited by 5

Locally and Structurally Private Graph Neural Networks

Joshi, R. B., & Mishra, S. (2023) · Digital Threats Research and Practice · doi:10.1145/3624485 · Cited by 5

Concentrated Geo-Privacy

Liang, Y., & Yi, K. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623068 · Cited by 5

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

Longitudinal attacks against iterative data collection with local differential privacy

Gürsoy, M. E. (2024) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.4063 · Cited by 5

AAA: An Adaptive Mechanism for Locally Differentially Private Mean Estimation

Wei, F., Bao, E., Xiao, X., Yang, Y., & Ding, B. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3659437.3659442 · Cited by 5

Differentially private de-identifying textual medical document is compliant with challenging NLP analyses: Example of privacy-preserving ICD-10 code association

Tchouka, Y., Couchot, J.-F., Laiymani, D., Selles, P., & Rahmani, A. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200416 · Cited by 6

Model Trip: Enhancing Privacy and Fairness in Model Fusion Across Multi-Federations for Trustworthy Global Healthcare

Chen, Q., Chen, Y., Yan, B., Jiang, X., Zhang, X., Kang, Y., Zhang, T., Huang, W., Gao, C., Fan, L., et al. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00177 · Cited by 5

Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential Privacy

Horigome, H., Kikuchi, H., Fujita, M., & Yu, C.-M. (2024) · Applied Sciences · doi:10.3390/app14146368 · Cited by 5

PrivFusion: Privacy-Preserving Model Fusion via Decentralized Federated Graph Matching

Chen, Q., Chen, Y., Jiang, X., Zhang, T., Dai, W., Huang, W., Yan, B., Yan, Z., Wang, L., & Ye, B. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3430819 · Cited by 5

Data-Driven Software Security: Models and Methods

Erlingsson, Ú. (2016) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf.2016.40 · Cited by 5

Constrained Private Mechanisms for Count Data

Cormode, G., Kulkarni, T., & Srivastava, D. (2018) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2018.00081 · Cited by 5

A criterion for privacy protection in data collection and its attainment via randomized response procedures

Chai, J., & Nayak, T. K. (2018) · Electronic Journal of Statistics · doi:10.1214/18-ejs1508 · Cited by 5

DeepFP: A Deep Learning Framework For User Fingerprinting via Mobile Motion Sensors

Amini, S., Noroozi, V., Bahaadini, S., Yu, P. S., & Kanich, C. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622372 · Cited by 5

ϵ KTELO

Zhang, D., McKenna, R., Kotsogiannis, I., Bissias, G. D., Hay, M., Machanavajjhala, A., & Miklau, G. (2020) · ACM Transactions on Database Systems · doi:10.1145/3362032 · Cited by 5

Privacy-preserving Crowd-sensed Trust Aggregation in the User-centeric Internet of People Networks

Azad, M. A., Perera, C., Bag, S., Barhamgi, M., & Hao, F. (2020) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3390860 · Cited by 5

Tracy-Widom law for the extreme eigenvalues of large signal-plus-noise matrices

Zhang, Z., Liu, Y., & Pan, G. (2023) · Bernoulli · doi:10.3150/23-bej1604 · Cited by 5

Research on a Data Desensitization Algorithm of Blockchain Distributed Energy Transaction Based on Differential Privacy

Li, Y., Li, N., & Xia, Y. (2019) · IEEE International Conference on Advanced Power System Automation and Protection (APAP) · doi:10.1109/apap47170.2019.9224935 · Cited by 5

Statistical query complexity of manifold estimation

Aamari, E., & Knop, A. (2021) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3406325.3451135 · Cited by 5

Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms

Ding, Z., Wang, Y., Xiao, Y., Wang, G., Zhang, D., & Kifer, D. (2022) · The VLDB Journal · doi:10.1007/s00778-022-00728-2 · Cited by 5

Differentially Private Federated Learning: Algorithm, Analysis and Optimization

Wei, K., Li, J., Ma, C., Ding, M., & Poor, H. V. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_3 · Cited by 7

GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel

Liu, H., Peng, C., Tian, Y., Long, S., Tian, F., & Wu, Z. (2022) · Entropy · doi:10.3390/e24030430 · Cited by 5

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

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