Differentially private image classification using support vector machine and differential privacy

Learning with Kernels

Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934 · see citing works

k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571 · see citing works

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 · see citing works

Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · see citing works

The Algorithmic Foundations of Differential Privacy

Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557 · see citing works

<i>L</i> -diversity: Privacy beyond <i>k</i> -anonymity

Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617 · see citing works

Differential Privacy: A Survey of Results

Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542 · see citing works

t-Closeness: Privacy Beyond k-Anonymity and l-Diversity

Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451 · see citing works

Privacy-preserving deep learning

Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356 · see citing works

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 · see citing works

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 · see citing works

Privacy-preserving data mining

Agrawal, R., & Srikant, R. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335438 · Cited by 1,719 · see citing works

Untitled work

(2011) · PubMed · Cited by 990 · see citing works

Privacy-preserving logistic regression

Chaudhuri, K., & Monteleoni, C. (2008) · Cited by 526 · see citing works

Privacy-Preserving Data Mining

Thomas, D. (2008) · The Kluwer international series on advances in database systems · doi:10.1007/978-0-387-70992-5 · Cited by 400 · see citing works

Using randomized response techniques for privacy-preserving data mining

Du, W. K., & Zhan, Z. (2003) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/956750.956810 · Cited by 286 · see citing works

Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

Rubinstein, B. I. P., Bartlett, P. L., Huang, L., & Taft, N. (2012) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v4i1.612 · Cited by 264 · see citing works

Towards practical differential privacy for SQL queries

Johnson, N., Near, J. P., & Song, D. X. (2018) · Proceedings of the VLDB Endowment · doi:10.1145/3187009.3177733 · Cited by 258 · see citing works

Privacy-Preserving Machine Learning Algorithms for Big Data Systems

Xu, K., Yue, H., Guo, L., Guo, Y., & Fang, Y. M. (2015) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2015.40 · Cited by 100 · see citing works

Differential Privacy and Applications

Zhu, T., Li, G., Zhou, W., & Yu, P. S. (2017) · Advances in information security · doi:10.1007/978-3-319-62004-6 · Cited by 92 · see citing works

Sample Complexity Bounds for Differentially Private Learning.

Chaudhuri, K., & Hsu, D. (2011) · PubMed · Cited by 71 · see citing works

Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems

Choi, W.-S., Tomei, M., Vicarte, J. R. S., Hanumolu, P. K., & Kumar, R. (2018) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2018.00053 · Cited by 54 · see citing works

Big Data and Differential Privacy

Attoh-Okine, N. (2017) · Wiley eBooks · doi:10.1002/9781119229070 · Cited by 40 · see citing works

“Privacy-preserving quantum machine learning using differential privacy,”

Senekane, M., Mafu, M., & Taele, B. M. (2017) · 2017 IEEE AFRICON · doi:10.1109/afrcon.2017.8095692 · Cited by 25 · see citing works

Smart Agriculture Cloud Using AI Based Techniques

Junaid, M., Shaikh, A., Hassan, M. U., Alghamdi, A., Rajab, K. D., Reshan, M. S. A., & Alkinani, M. H. (2021) · Energies · doi:10.3390/en14165129 · Cited by 68 · see citing works

Privacy-Preserving Multi-Class Support Vector Machine Model on Medical Diagnosis

Chen, Y., Mao, Q., Wang, B., Duan, P., Zhang, B., & Hong, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3157592 · Cited by 63 · see citing works

Startups and Consumer Purchase Behavior: Application of Support Vector Machine Algorithm

Ebrahimi, P., Salamzadeh, A., Soleimani, M., Khansari, S. M., Zarea, H., & Fekete-Farkas, M. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6020034 · Cited by 36 · see citing works

Predicting the depression in university students using stacking ensemble techniques over oversampling method

Vergaray, A. D., Herrera, J. C., Cornelio, J. B., Carranza, A. R. L., & Sánchez, C. F. P. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101295 · Cited by 38 · see citing works

A Differential Privacy Support Vector Machine Classifier Based on Dual Variable Perturbation

Zhang, Y., Hao, Z., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2929680 · Cited by 38 · see citing works

Stacking ensemble learning model for predict anxiety level in university students using balancing methods

Vergaray, A. D., Arroyo-Paz, A., Cornelio, J. B., Pérez, O. G. A., & Pinto, J. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101340 · Cited by 38 · see citing works

Privacy preserving rule-based classifier using modified artificial bee colony algorithm

Zorarpacı, E., & Özel, S. A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115437 · Cited by 23 · see citing works

Systematic Survey: Secure and Privacy-Preserving Big Data Analytics in Cloud

Rajan, A. A., & Vetriselvi, V. (2023) · Journal of Computer Information Systems · doi:10.1080/08874417.2023.2176946 · Cited by 22 · see citing works

Efficient differentially private kernel support vector classifier for multi-class classification

Park, J.-G., Choi, Y., Byun, J., Lee, J., & Park, S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.10.075 · Cited by 21 · see citing works

Privacy preserving classification over differentially private data

Zorarpacı, E., & Özel, S. A. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1399 · Cited by 18 · see citing works

Differentially private 1R classification algorithm using artificial bee colony and differential evolution

Zorarpacı, E., & Özel, S. A. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103813 · Cited by 16 · see citing works

Differentially private regression analysis with dynamic privacy allocation

Pan, K., Gong, M., Feng, K., & Wang, K. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106795 · Cited by 16 · see citing works

Quantum technology for development framework as a tool for science diplomacy

Mafu, M., & Senekane, M. (2023) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2023.1279376 · Cited by 13 · see citing works

Factors influencing green, environmentally-friendly consumer behaviour

Suhaeni, S., Wulandari, E., Turnip, A., & Deliana, Y. (2024) · Open Agriculture · doi:10.1515/opag-2022-0269 · Cited by 10 · see citing works

Privacy-Preserving Classification Scheme Based on Support Vector Machine

Mao, Q., Chen, Y., Duan, P., Zhang, B., Hong, Z., & Wang, B. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3150785 · Cited by 9 · see citing works

Noisy, Intermediate-Scale Quantum Computing and Industrial Revolution 4.0

Senekane, M., Maseli, M., & Taele, B. M. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-48230-5_9 · Cited by 8 · see citing works

Deployment of Differential Privacy for Application in Artificial Intelligence

Senekane, M. (2021) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet52533.2021.9698473 · Cited by 5 · see citing works

Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations

Jafarigol, E., Trafali̇s, T. B., Razzaghi, T., & Zamankhani, M. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-74006-0_4 · Cited by 4 · see citing works

Ontology Learning using Hybrid Machine Learning Algorithms for Disaster Risk Management

Contreras, J. O., Ballera, M. A., & Festijo, E. D. (2020) · doi:10.1145/3432291.3432306 · Cited by 4 · see citing works

A comparative Study on AI-Driven Anonymization Techniques for Protecting Personal Data

-, M. P., -, A. K. U., -, M. R., & -, J. D. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i04.24770 · Cited by 5 · see citing works

Masked image: Visually protected image dataset privacy-preserving scheme for convolutional neural networks

Kou, X., Wang, F., Zhu, H., Zheng, Y., Yang, X., & Liu, Z. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01718-7 · Cited by 3 · see citing works

Clinical applications of artificial intelligence in diabetes management: A bibliometric analysis and comprehensive review

Vergaray, A. D., Olivos-López, A. J., Pizarro, M. C., Abad-Escalante, K. M., Ortiz, P. G. C., Apaza, R. D. M., Ruiz-Baca, J., & Sánchez-Chávez, J. P. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101567 · Cited by 2 · see citing works

Differential Privacy Protection for Support Vector Machines for Nonlinear Classification

Huang, Y., Yang, G., Bai, Y., & Dai, H. (2022) · Security and Communication Networks · doi:10.1155/2022/7941915 · Cited by 2 · see citing works

Prediction of Middle-Aged Unhealthy Facial Skin Using VGG19 and Support Vector Machine Models

Wiryasaputra, R., Huang, C., Williyanto, R., & Yang, C. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-9582-8_25 · Cited by 2 · see citing works

Classification of Alphabetic Handwriting of Pre-toddlers Using Support Vector Machine Method

Riyadi, S. A. P., Gunawan, P. H., & Ihsan, A. F. (2023) · IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) · doi:10.1109/comnetsat59769.2023.10420648 · Cited by 1 · see citing works

Approximate DBSCAN under Differential Privacy

Qiu, Y., & Yi, K. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725265 · Cited by 1 · see citing works

PriSEG: A Privacy-Preserving Scheme for Image Segmentation System

He, Z., Luo, Y., Huang, K., Liu, L., & Fu, S. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3674217 · Cited by 1 · see citing works

MIXTURE FEATURE EXTRACTION BASED ON LOCAL BINARY PATTERN AND GREY-LEVEL CO-OCCURRENCE MATRIX TECHNIQUES FOR MOUTH EXPRESSION RECOGNITION

Pramunendar, R. A., Prabowo, D. P., & Sari, Y. (2022) · Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) · doi:10.20527/jtiulm.v7i2.145 · Cited by 1 · see citing works

Deep Convolutional Generative Adversarial Networks for Imbalance Medical Image Classification.

Ismail, A. R., Khalili, A. S. M., Rahim, N. F. A., & Nisa, S. Q. (2023) · International Journal on Perceptive and Cognitive Computing · doi:10.31436/ijpcc.v9i2.409 · Cited by 1 · see citing works

Assessment of Spatial and temporal changes in land use using remote sensing (case study: Jayransoo rangeland, North Khorasan)

Nadaf, M., Omidipour, R., & Sobhani, H. (2024) · Pizhūhish va fanāvarī-i muḥīṭ-i zīst. · doi:10.61186/jert.43616.8.14.131 · Cited by 1 · see citing works

An Interpretable Graph-Based Mapping of Trustworthy Machine Learning Research

Derzsy, N., Majumdar, S., & Malik, R. (2021) · Springer proceedings in complexity · doi:10.1007/978-3-030-81854-8_7 · Cited by 1 · see citing works

Image Features Anonymization for Privacy Aware Machine Learning

Jaidan, N., & Duong, L. T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64583-0_58

Privacy Preserving in Big Data: Summary of Classification techniquesPreprint

Ruogang, T. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3909074

Privacy-Preserving Training of Support Vector Machines via Secure Multiparty Computation

Jaramillo, D. C., Madrigal, H. D. V., Escudero, D. E., & Morales, F. A. (2025) · ACM Transactions on Privacy and Security · doi:10.1145/3749373

A Comparative Study of SVM and CNN for Pneumonia Detection from Radiographic Images: Computational Efficiency and Diagnostic Precision

Sevani, N., Seruni, Muljono, J. B., Kean, S. D., & Wiryasaputra, R. (2025) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icast68191.2025.11300234

Removal of Fluoride Using Activated Alumina: Optimization and Modelling

Dutta, N., & Dey, P. (2026) · Lecture notes in civil engineering · doi:10.1007/978-981-95-2893-6_4

Enhancing face recognition privacy through the integration of differential privacy and convolutional neural network

Hossain, M. M., & Rahman, M. M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0353565

Differential Privacy Protection for Support Vector Machines for Nonlinear Classification

Huang, Y., Yang, G., Bai, Y., & Dai, H. (2022) · Security and Communication Networks · doi:10.1155/2022/7941915 · Cited by 2 · see citing works

Multi-Class Support Vector Machine with Differential Privacy

Park, J.-G., Choi, Y., & Lee, J. (2025) · neural information processing systems · doi:10.52202/085713-2449

Differential Private Multiple Classification Algorithm for SVM

Wang, H., & Li, S. (2018) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2018.8691193 · Cited by 4 · see citing works

Efficient differentially private kernel support vector classifier for multi-class classification

Park, J.-G., Choi, Y., Byun, J., Lee, J., & Park, S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.10.075 · Cited by 21 · see citing works

Unlocking High-Accuracy Differentially Private Image Classification through ScalePreprint

De, S., Berrada, L., Hayes, J., Smith, S. L., & Balle, B. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.13650 · Cited by 45 · see citing works