Differential Privacy

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

The Algorithmic Foundations of Differential Privacy

Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856

Practical Secure Aggregation for Privacy-Preserving Machine Learning

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956

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

Fairness through awareness

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961

Decentralizing Privacy: Using Blockchain to Protect Personal Data

Zyskind, G., Nathan, O., & Pentland, A. '. (2015) · 2015 IEEE Security and Privacy Workshops · doi:10.1109/spw.2015.27 · Cited by 2,617

Robust De-anonymization of Large Sparse Datasets

Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372

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

Privacy-preserving data publishing: A survey of recent developments

Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704

A survey on security and privacy of federated learning

Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480

The economics of privacy

Acquisti, A., Taylor, C., & Wagman, L. (2016) · Journal of Economic Literature · doi:10.1257/jel.54.2.442 · Cited by 1,283

Untitled work

(2016) · International Conference on Machine Learning · Cited by 1,270

Adversarial machine learning

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194

Federated Learning for Mobile Keyboard Prediction

Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148

A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly

Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083

Differentially Private Federated Learning: A Client Level Perspective

Geyer, R. C., Klein, T., & Nabi, M. (2017) · arXiv · doi:10.48550/arxiv.1712.07557 · Cited by 1,052

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018) · 2018 IEEE 31st Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2018.00027 · Cited by 1,001

Untitled work

(2011) · PubMed · Cited by 990

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912

Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology

Dagher, G. G., Mohler, J., Milojkovic, M., & Marella, P. B. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2018.02.014 · Cited by 854

Machine Unlearning

Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., & Papernot, N. (2021) · 2021 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp40001.2021.00019 · Cited by 810

Applied Federated Learning: Improving Google Keyboard Query Suggestions

Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., & Beaufays, F. (2018) · arXiv · doi:10.48550/arxiv.1812.02903 · Cited by 684

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

Ni, J., Zhang, K., Lin, X., & Shen, X. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2762345 · Cited by 659

Challenges in deploying machine learning: A survey of case studies

Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2023) · ACM Computing Surveys · doi:10.1145/3533378 · Cited by 638

Unique in the shopping mall: On the reidentifiability of credit card metadata

de Montjoye, Y.-A., Radaelli, L., Singh, V. K., & Pentland, A. “. (2015) · Science · doi:10.1126/science.1256297 · Cited by 570

Differential Privacy Techniques for Cyber Physical Systems: A Survey

Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564

Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions

Hassan, M. U., Rehmani, M. H., & Chen, J. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.060 · Cited by 520

Security services using blockchains: a state of the art survey

Salman, T., Zolanvari, M., Erbad, A., Jain, R., & Samaka, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2863956 · Cited by 502

Big Data ethics

Zwitter, A. (2014) · Big Data & Society · doi:10.1177/2053951714559253 · Cited by 465

Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing

Qu, Y., Gao, L., Luan, T. H., Xiang, Y., Yu, S., Li, B., & Zheng, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2977383 · Cited by 451

A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View

Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018) · IEEE Access · doi:10.1109/access.2018.2805680 · Cited by 421

The composition theorem for differential privacy

Kairouz, P., Oh, S., & Viswanath, P. (2017) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2685505 · Cited by 408

Recent advances in feature selection and its applications

Li, Y., Li, T., & Liu, H. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1059-8 · Cited by 400

Harnessing the power of synthetic data in healthcare: innovation, application, and privacy

Giuffrè, M., & Shung, D. L. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00927-3 · Cited by 392

Generative Adversarial Networks in Time Series: A Systematic Literature Review

Brophy, E., Wang, Z., She, Q., & Ward, T. (2023) · ACM Computing Surveys · doi:10.1145/3559540 · Cited by 337

Slicing: A New Approach for Privacy Preserving Data Publishing

Li, T., Li, N., Zhang, J., & Molloy, I. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.236 · Cited by 312

Big privacy: challenges and opportunities of privacy study in the age of big data

Yu, S. (2016) · IEEE Access · doi:10.1109/access.2016.2577036 · Cited by 307

The pursuit of citizens' privacy: a privacy-aware smart city is possible

Martinez-Balleste, A., Perez-martinez, P., & Solanas, A. (2013) · IEEE Communications Magazine · doi:10.1109/mcom.2013.6525606 · Cited by 307

Multi-Agent Risks from Advanced AI

Hammond, L., Chan, A., Clifton, J., Hoelscher-Obermaier, J., Khan, A., McLean, E., Smith, C., Barfuss, W., Foerster, J., Gavenčiak, T., et al. (2025) · arXiv · doi:10.48550/arxiv.2502.14143 · Cited by 305

"You Might Also Like:" Privacy Risks of Collaborative Filtering

Calandrino, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2011) · 2011 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2011.40 · Cited by 311

A taxonomy of privacy-preserving record linkage techniques

Vatsalan, D., Christen, P., & Verykios, V. S. (2013) · Information Systems · doi:10.1016/j.is.2012.11.005 · Cited by 295

A review of secure and privacy-preserving medical data sharing

Jin, H., Luo, Y., Li, P., & Mathew, J. (2019) · IEEE Access · doi:10.1109/access.2019.2916503 · Cited by 293

Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions

Eckhoff, D., & Wagner, I. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2748998 · Cited by 287

Privacy in the Genomic Era

Naveed, M., Ayday, E., Clayton, E. W., Fellay, J., Gunter, C. A., Hubaux, J.-P., Malin, B. A., & Wang, X. (2015) · ACM Computing Surveys · doi:10.1145/2767007 · Cited by 261

Differentially Private Distributed Optimization

Huang, Z., Mitra, S., & Vaidya, N. (2015) · Proceedings of the 16th International Conference on Distributed Computing and Networking · doi:10.1145/2684464.2684480 · Cited by 266

Exposed! A Survey of Attacks on Private Data

Dwork, C., Smith, A., Steinke, T., & Ullman, J. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054123 · Cited by 259

Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., & Thakurta, A. (2019) · Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms · doi:10.1137/1.9781611975482.151 · Cited by 264

LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision, and the Road Ahead

He, J., Treude, C., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712003 · Cited by 246

LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead

He, J., Treude, C., & Lo, D. (2024) · arXiv · doi:10.48550/arxiv.2404.04834 · Cited by 246

Human-Data Interaction: The Human Face of the Data-Driven Society

Mortier, R., Haddadi, H., Henderson, T., McAuley, D., & Crowcroft, J. (2014) · doi:10.2139/ssrn.2508051 · Cited by 247

FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models

Liu, G., Ma, X., Yang, Y., Wang, C., & Liu, J. (2021) · 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) · doi:10.1109/iwqos52092.2021.9521274 · Cited by 248

Untitled work

sharma (2018) · doi:10.1109/mic.2018.112102519 · Cited by 220

Differential Privacy Has Disparate Impact on Model Accuracy

Bagdasaryan, E., & Shmatikov, V. (2019) · arXiv · doi:10.48550/arxiv.1905.12101 · Cited by 219

When Machine Learning Meets Privacy in 6G: A Survey

Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211

How Much Is Enough? Choosing ε for Differential Privacy

Lee, J., & Clifton, C. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24861-0_22 · Cited by 201

Artificial Intelligence Security: Threats and Countermeasures

Hu, Y., Kuang, W., Qin, Z., Li, K., Zhang, J., Gao, Y., Li, W., & Li, K. (2023) · ACM Computing Surveys · doi:10.1145/3487890 · Cited by 196

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Zhu, T., Ye, D., Wang, W., Zhou, W., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3014246 · Cited by 219

Federated learning for the healthcare metaverse: Concepts, applications, challenges, and future directions

Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Wood, A., Najarian, K., & Kahrobaei, D. (2021) · ACM Computing Surveys · doi:10.1145/3394658 · Cited by 179

Automating E-government services with artificial intelligence

Al-Mushayt, O. S. (2019) · IEEE Access · doi:10.1109/access.2019.2946204 · Cited by 160

Certified Data Removal from Machine Learning Models

Guo, C., Goldstein, T., Hannun, A., & van der Maaten, L. (2019) · arXiv · doi:10.48550/arxiv.1911.03030 · Cited by 160

Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy

Mulligan, D. K., Koopman, C., & Doty, N. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2016.0118 · Cited by 149

Enhancing data utility in differential privacy via microaggregation-based $$k$$ k -anonymity

Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., & Martínez, S. (2014) · The VLDB Journal · doi:10.1007/s00778-014-0351-4 · Cited by 149

Deriving genomic diagnoses without revealing patient genomes

Jagadeesh, K. A., Wu, D. J., Birgmeier, J. A., Boneh, D., & Bejerano, G. (2017) · Science · doi:10.1126/science.aam9710 · Cited by 134

Federated Tensor Factorization for Computational Phenotyping

Kim, Y., Sun, J., Yu, H., & Jiang, X. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098118 · Cited by 128

Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing

Du, M., Wang, K., Xia, Z., & Zhang, Y. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2829886 · Cited by 111

A Review of Privacy-Preserving Federated Learning for the Internet-of-Things

Briggs, C., Fan, Z., & Andras, P. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-70604-3_2 · Cited by 101

FairSquare: probabilistic verification of program fairness

Albarghouthi, A., D'Antoni, L., Drews, S., & Nori, A. V. (2017) · Proceedings of the ACM on Programming Languages · doi:10.1145/3133904 · Cited by 94

Discrimination- and privacy-aware patterns

Hajian, S., Domingo-Ferrer, J., Monreale, A., Pedreschi, D., & Giannotti, F. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0393-7 · Cited by 93

Untitled work

(2012) · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · Cited by 93

Machine learning and genomics: precision medicine versus patient privacy

Azencott, C.-A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2017.0350 · Cited by 89

Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

Long, G., Shen, T., Tan, Y., Gerrard, L., Clarke, A., & Jiang, J. (2022) · Humanity Driven AI · doi:10.1007/978-3-030-72188-6_6 · Cited by 85

Predictive privacy: Collective data protection in the context of artificial intelligence and big data

Mühlhoff, R. (2023) · Big Data & Society · doi:10.1177/20539517231166886 · Cited by 85

Privacy Amplification by Iteration

Feldman, V., Mironov, I., Talwar, K., & Thakurta, A. (2018) · 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) · doi:10.1109/focs.2018.00056 · Cited by 80

C‐sanitized: A privacy model for document redaction and sanitization

Sánchez, D., & Batet, M. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23363 · Cited by 80

Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States

Wang, S., Jiang, X., Singh, S., Marmor, R., Bonomi, L., Fox, D., Dow, M., & Ohno‐Machado, L. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13259 · Cited by 78

Optimal Lower Bound for Differentially Private Multi-party Aggregation

Chan, T.-H. H., Shi, E., & Song, D. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33090-2_25 · Cited by 82

Free-riders in Federated Learning: Attacks and Defenses

Lin, J., Du, M., & Liu, J. (2019) · arXiv · doi:10.48550/arxiv.1911.12560 · Cited by 76

The tao of inference in privacy-protected databases

Bindschaedler, V., Grubbs, P., Cash, D., Ristenpart, T., & Shmatikov, V. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3236187.3236217 · Cited by 75

A survey on differential privacy for medical data analysis

Liu, W., Zhang, Y., Yang, H., & Meng, Q. (2024) · Annals of Data Science · doi:10.1007/s40745-023-00475-3 · Cited by 66

Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy

Salamatian, S., Zhang, A., du Pin Calmon, F., Bhamidipati, S., Fawaz, N., Kveton, B., Oliveira, P., & Taft, N. (2015) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2015.2442227 · Cited by 66

Secure and robust machine learning for healthcare: A survey

Qayyum, A., Qadir, J., Bilal, M., & Al-Fuqaha, A. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3013489 · Cited by 59

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

Froelicher, D., Troncoso-Pastoriza, J. R., Sousa, J. S., & Hubaux, J.-P. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2976612 · Cited by 59

A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy

Manzoor, H. U., Shabbir, A., Chen, A., Flynn, D., & Zoha, A. (2024) · Future Internet · doi:10.3390/fi16100374 · Cited by 58

Differentially private data sharing in a cloud federation with blockchain

Yang, M., Margheri, A., Hu, R., & Sassone, V. (2018) · IEEE Cloud Computing · doi:10.1109/mcc.2018.064181122 · Cited by 58

Improved l-diversity: scalable anonymization approach for privacy preserving big data publishing

Mehta, B. B., & Rao, U. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2019.08.006 · Cited by 57

Learning Anonymized Representations with Adversarial Neural Networks

Feutry, C., Piantanida, P., Bengio, Y., & Duhamel, P. (2018) · arXiv · doi:10.48550/arxiv.1802.09386 · Cited by 52

SafePath: Differentially-private publishing of passenger trajectories in transportation systems

Al-Hussaeni, K., Fung, B. C. M., Iqbal, F., Dagher, G. G., & Park, E. G. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.07.007 · Cited by 52

Privacy-preserving aggregation of personal health data streams

Kim, J. W., Jang, B., & Yoo, H. (2018) · PLOS ONE · doi:10.1371/journal.pone.0207639 · Cited by 49

Data flow management and compliance in cloud computing

Singh, J., Powles, J., Pasquier, T., & Bacon, J. (2015) · IEEE Cloud Computing · doi:10.1109/mcc.2015.69 · Cited by 48

Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning

Hagendorff, T. (2021) · Minds and Machines · doi:10.1007/s11023-021-09573-8 · Cited by 47

P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification

Wu, B., Zhao, S., Sun, G., Zhang, X., Su, Z., Zeng, C., & Liu, Z. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00220 · Cited by 44

Improving k-anonymity based privacy preservation for collaborative filtering

Wei, R., Tian, H., & Shen, H. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.02.017 · Cited by 44

An improved matrix factorization with local differential privacy based on piecewise mechanism for recommendation systems

Wang, Y., Gao, M., Ran, X., Ma, J., & Zhang, L. Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119457 · Cited by 42

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

Zantedeschi, V., Bellet, A., & Tommasi, M. (2019) · arXiv · doi:10.48550/arxiv.1901.08460 · Cited by 38

Backdoor attacks and defenses in feature-partitioned collaborative learning

Liu, Y., Yi, Z., & Chen, T. (2020) · arXiv · doi:10.48550/arxiv.2007.03608 · Cited by 38

New SVD-based collaborative filtering algorithms with differential privacy

Xian, Z., Li, Q., Huang, X., & Li, L. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-162053 · Cited by 35

Matrix factorization recommender based on adaptive Gaussian differential privacy for implicit feedback

Liu, H., Wang, Y., Zhang, Z., Deng, J., Chen, C., & Zhang, L. Y. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103720 · Cited by 34

Improving Data Utility Through Game Theory in Personalized Differential Privacy

Cui, L., Qu, Y., Nosouhi, M. R., Yu, S., Niu, J.-W., & Xie, G. (2019) · Journal of Computer Science and Technology · doi:10.1007/s11390-019-1910-3 · Cited by 31