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Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees.
k-anonymity: A model for protecting privacy
Sweeney, L · 2002
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Wherefore art thou r3579x? anonymized social networks, hidden patterns, and structural steganography
Backstrom, L., Dwork, C., and Kleinberg, J · 2007
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t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., and Venkatasubramanian, S · 2007
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L-diversity: Privacy beyond k-anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., and Venkitasubramaniam, M · 2007
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Composition attacks and auxiliary information in data privacy
Ganta, S. R., Kasiviswanathan, S. P., and Smith, A · 2008
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Robust De-anonymization of Large Sparse Datasets
Narayanan, A., and Shmatikov, V · 2008
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The Algorithmic Foundations of Differential Privacy
Dwork, C., and Roth, A · 2014
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Genetic interactions contribute less than additive effects to quantitative trait variation in yeast
Bloom, J. S., Kotenko, I., Sadhu, M. J., Treusch, S., Albert, F. W., and Kruglyak, L · 2015
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Towards Making Systems Forget with Machine Unlearning
Cao, Y., and Yang, J · 2015
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Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Concentrated differential privacy
Dwork, C., and Rothblum, G. N · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., and Talwar, K · 2016
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Wasserstein Generative Adversarial Networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Improved Training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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Rényi Differential Privacy
Mironov, I · 2017
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DP-EM: Differentially Private Expectation Maximization
Park, M., Foulds, J., Choudhary, K., and Welling, M · 2017
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Adaptive laplace mechanism: Differential privacy preservation in deep learning
Phan, N., Wu, X., Hu, H., and Dou, D · 2017
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Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Privacy in Neural Network Learning: Threats and Countermeasures
Chang, S., and Li, C · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J., and Carbin, M · 2018
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Adversarial robustness toolbox v1.2.0
Nicolae, M.-I., Sinn, M., Tran, M. N., Buesser, B., Rawat, A., Wistuba, M., Zantedeschi, V., Baracaldo, N., Chen, B., Ludwig, H., Molloy, I., and Edwards, B · 2018
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The book of why: the new science of cause and effect
Pearl, J., and Mackenzie, D · 2018
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Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten
Villaronga, E. F., Kieseberg, P., and Li, T · 2018
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Differentially private generative adversarial network
Xie, L., Lin, K., Wang, S., Wang, F., and Zhou, J · 2018
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
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Privacy Preserving Synthetic Data Release Using Deep Learning
Abay, N. C., Zhou, Y., Kantarcioglu, M., Thuraisingham, B., and Sweeney, L · 2019
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Differentially Private Mixture of Generative Neural Networks
Acs, G., Melis, L., Castelluccia, C., and De Cristofaro, E · 2019
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A layer-wise Perturbation based Privacy Preserving Deep Neural Networks
Adesuyi, T. A., and Kim, B. M · 2019
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Differential Privacy Has Disparate Impact on Model Accuracy
Bagdasaryan, E., Poursaeed, O., and Shmatikov, V · 2019
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Robust anomaly detection and backdoor attack detection via differential privacy
Du, M., Jia, R., and Song, D · 2019
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Generalised differential privacy for text document processing
Fernandes, N., Dras, M., and McIver, A · 2019
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Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text
Feyisetan, O., Diethe, T., and Drake, T · 2019
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Differential Privacy in Deep Learning: An Overview
Ha, T., Dang, T. K., Dang, T. T., Truong, T. A., and Nguyen, M. T · 2019
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Evaluating differentially private machine learning in practice
Jayaraman, B., and Evans, D · 2019
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Pate-gan: Generating synthetic data with differential privacy guarantees
Jordon, J., Yoon, J., and Van Der Schaar, M · 2019
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Certified Robustness to Adversarial Examples with Differential Privacy
Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., and Jana, S · 2019
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Privacy-preserving lightweight face recognition
Li, Y., Wang, Y., and Li, D · 2019
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Private selection from private candidates
Liu, J., and Talwar, K · 2019
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PPGAN: Privacy-Preserving Generative Adversarial Network
Liu, Y., Peng, J., Yu, J. J., and Wu, Y · 2019
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Layer-Wise Relevance Propagation: An Overview
Montavon, G., Binder, A., Lapuschkin, S., Samek, W., and Müller, K.-R · 2019
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An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack
Park, C., Hong, D., and Seo, C · 2019
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Phan, N., Vu, M., Liu, Y., Jin, R., Dou, D., Wu, X., and Thai, M. T · 2019
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A Survey on Deep Learning Techniques for Privacy-Preserving
Tanuwidjaja, H. C., Choi, R., and Kim, K · 2019
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Differentially Private Deep Learning for Load Forecasting on Smart Grid
Ustundag Soykan, E., Bilgin, Z., Ersoy, M. A., and Tomur, E · 2019
Cited alongside, same era.
Private Model Compression via Knowledge Distillation
Wang, J., Bao, W., Sun, L., Zhu, X., Cao, B., and Yu, P. S · 2019
Cited alongside, same era.
Differentially-Private Deep Learning from an optimization Perspective
Xiang, L., Yang, J., and Li, B · 2019
Cited alongside, same era.
GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy
Xu, C., Ren, J., Zhang, D., Zhang, Y., Qin, Z., and Ren, K · 2019
Cited alongside, same era.
Differentially Private Model Publishing for Deep Learning
Yu, L., Liu, L., Pu, C., Gursoy, M. E., and Truex, S · 2019
Cited alongside, same era.
Why gradient clipping accelerates training: A theoretical justification for adaptivity
When Machine Learning Meets Privacy: A Survey and Outlook
Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., and Lin, Z · 2021
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Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
Nasr, M., Songi, S., Thakurta, A., Papernot, N., and Carlin, N · 2021
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Privacy attacks against deep learning models and their countermeasures
Shafee, A., and Awaad, T. A · 2021
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Analysis of Application Examples of Differential Privacy in Deep Learning
Shen, Z., and Zhong, T · 2021
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GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things
Yu, J., Xue, H., Liu, B., Wang, Y., Zhu, S., and Ding, M · 2021
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Graph Embedding for Recommendation against Attribute Inference Attacks
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Zhang, J., He, T., Sra, S., and Jadbabaie, A · 2019
Cited alongside, same era.
Differential Privacy Preservation in Deep Learning: Challenges, Opportunities and Solutions
Zhao, J., Chen, Y., and Zhang, W · 2019
Cited alongside, same era.
BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks
Zheng, H., Ye, Q., Hu, H., Fang, C., and Shi, J · 2019
Cited alongside, same era.
Preech: A system for privacy-preserving speech transcription
Ahmed, S., Chowdhury, A. R., Fawaz, K., and Ramanathan, P · 2020
Cited alongside, same era.
A review of privacy-preserving techniques for deep learning
Boulemtafes, A., Derhab, A., and Challal, Y · 2020
Cited alongside, same era.
Privacy Preserving Face Recognition Utilizing Differential Privacy
Chamikara, M. A. P., Bertok, P., Khalil, I., Liu, D., and Camtepe, S · 2020
Cited alongside, same era.
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Chen, D., Orekondy, T., and Fritz, M · 2020
Cited alongside, same era.
Zhang, S., Yin, H., Chen, T., Huang, Z., Cui, L., and Zhang, X · 2021
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Data Privacy Preservation and Security in Smart Metering Systems
Abdalzaher, M. S., Fouda, M. M., and Ibrahem, M. I · 2022
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Privid: Practical, { \{ Privacy-Preserving } \} video analytics queries
Cangialosi, F., Agarwal, N., Arun, V., Narayana, S., Sarwate, A., and Netravali, R · 2022
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Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
Chen, C., Wu, H., Su, J., Lyu, L., Zheng, X., and Wang, L · 2022
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Private Empirical Risk Minimization With Analytic Gaussian Mechanism for Healthcare System
Ding, J., Errapotu, S. M., Guo, Y., Zhang, H., Yuan, D., and Pan, M · 2022
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Privacy-Preserving Analysis for Remote Video Anomaly Detection in Real Life Environments
Giorgi, G., Abbasi, W., and Saracino, A · 2022
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Differential Privacy for Deep and Federated Learning: A Survey
Ouadrhiri, A. E., and Abdelhadi, A · 2022
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Privacy-Preserving Machine Learning Using Cryptography
Rechberger, C., and Walch, R · 2022
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Synthetic data–anonymisation groundhog day
Stadler, T., Oprisanu, B., and Troncoso, C · 2022
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Differentially private synthetic medical data generation using convolutional GANs
Torfi, A., Fox, E. A., and Reddy, C. K · 2022
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Considerations for differentially private learning with large-scale public pretraining
Tramèr, F., Kamath, G., and Carlini, N · 2022
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Debugging differential privacy: A case study for privacy auditing
Tramer, F., Terzis, A., Steinke, T., Song, S., Jagielski, M., and Carlini, N · 2022
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Deep Learning: Differential Privacy Preservation in the Era of Big Data
Vasa, J., and Thakkar, A · 2022
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IdentityDP: Differential private identification protection for face images
Wen, Y., Liu, B., Ding, M., Xie, R., and Song, L · 2022
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Monitoring-Based Differential Privacy Mechanism Against Query Flooding-Based Model Extraction Attack
Yan, H., Li, X., Li, H., Li, J., Sun, W., and Li, F · 2022
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Griffin: Real-Time Network Intrusion Detection System via Ensemble of Autoencoder in SDN
Yang, L., Song, Y., Gao, S., Hu, A., and Xiao, B · 2022
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Exploration of Membership Inference Attack on Convolutional Neural Networks and Its Defenses
Yao, Y · 2022
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One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy
Ye, D., Shen, S., Zhu, T., Liu, B., and Zhou, W · 2022
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Protecting Decision Boundary of Machine Learning Model With Differentially Private Perturbation
Zheng, H., Ye, Q., Hu, H., Fang, C., and Shi, J · 2022
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More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Zhu, T., Ye, D., Wang, W., Zhou, W., and Yu, P. S · 2022
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Last accessed May 31, 2023 from https://cam-orl.co.uk/facedatabase.html/
At&t laboratories cambridge: The database of faces · 2023
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Last accessed June 7, 2023 from https://openai.com/chatgpt
Chatgpt · 2023
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Last accessed May 31, 2023 from https://github.com/ox-vgg/vgg_face2
Github: Vggface2 dataset for face recognition · 2023
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Last accessed May 31, 2023 from https://www.kaggle.com/c/acquire-valued-shoppers-challenge/data
Kaggle: Acquire valued shoppers challenge · 2023
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Last accessed May 31, 2023 from https://vis-www.cs.umass.edu/lfw/
Labeled faces in the wild · 2023
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Last accessed May 31, 2023 from https://archive.ics.uci.edu/ml/datasets/Adult
Uci machine learning repository: Adult data set · 2023
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Last accessed May 31, 2023 from https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
Uci machine learning repository: Breast cancer wisconsin (diagnostic) data set · 2023
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Last accessed May 31, 2023 from https://archive.ics.uci.edu/ml/datasets/Diabetes
Uci machine learning repository: Diabetes data set · 2023
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Last accessed May 31, 2023 from https://archive.ics.uci.edu/ml/datasets/Hepatitis
Uci machine learning repository: Hepatitis data set · 2023
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Last accessed May 31, 2023 from https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29
Uci machine learning repository: Statlog (german credit data) data set · 2023
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Differential privacy meets neural network pruning
Adamczewski, K., and Park, M · 2023
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A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
Blanco-Justicia, A., Sanchez, D., Domingo-Ferrer, J., and Muralidhar, K · 2023
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Challenges towards the next frontier in privacy
Cummings, R., Desfontaines, D., Evans, D., Geambasu, R., Jagielski, M., Huang, Y., Kairouz, P., Kamath, G., Oh, S., Ohrimenko, O., et al · 2023
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An empirical analysis of fairness notions under differential privacy
de Oliveira, A. S., Kaplan, C., Mallat, K., and Chakraborty, T · 2023
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A list of real-world uses of differential privacy, 2021
Desfontaines, D · 2023
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Cifar-10 and cifar-100 datasets
Krizhevsky, A · 2023
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Differentially private stochastic gradient descent via compression and memorization
Phong, L. T., and Phuong, T. T · 2023
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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., and Liu, Z · 2099
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