Fetching the paper…
Reading the bibliography…
Membership inference attacks aim to detect if a particular data point was used in training a model.
Information theory, inference and learning algorithms
MacKay, D. J · 2003
Earlier work this paper cites.
Differential privacy
Dwork, C · 2006
Earlier work this paper cites.
Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J. V., Stephan, D. A., Nelson, S. F., and Craig, D. W · 2008
Earlier work this paper cites.
Genomic privacy and limits of individual detection in a pool
Sankararaman, S., Obozinski, G., Jordan, M. I., and Halperin, E · 2009
Earlier work this paper cites.
The limits of individual identification from sample allele frequencies: theory and statistical analysis
Visscher, P. M. and Hill, W. G · 2009
Earlier work this paper cites.
Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Earlier work this paper cites.
Robust traceability from trace amounts
Dwork, C., Smith, A., Steinke, T., Ullman, J., and Vadhan, S · 2015
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Earlier work this paper cites.
Membership privacy in microrna-based studies
Backes, M., Berrang, P., Humbert, M., and Manoharan, P · 2016
Earlier work this paper cites.
An exploration of softmax alternatives belonging to the spherical loss family
De Brebisson, A. and Vincent, P · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., , and Sun, J · 2016
Earlier work this paper cites.
Large-margin softmax loss for convolutional neural networks
Liu, W., Wen, Y., Yu, Z., and Yang, M · 2016
Earlier work this paper cites.
Soft-margin softmax for deep classification
Liang, X., Wang, X., Lei, Z., Liao, S., and Z., L. S · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Earlier work this paper cites.
Property inference attacks on fully connected neural networks using permutation invariant representations
Ganju, K., Wang, Q., Yang, W., Gunter, C. A., and Borisov, N · 2018
Earlier work this paper cites.
Machine learning with membership privacy using adversarial regularization
Nasr, M., Shokri, R., and Houmansadr, A · 2018
Earlier work this paper cites.
Membership inference attack against differentially private deep learning model
Rahman, M. A., Rahman, T., Laganiere, R., Mohammed, N., and Wang, Y · 2018
Earlier work this paper cites.
Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
Earlier work this paper cites.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, U., Kos, J., and Song, D · 2019
Cited alongside, same era.
Memguard: Defending against black-box membership inference attacks via adversarial examples
Jia, J., Salem, A., Backes, M., Zhang, Y., and Gong, N. Z · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V · 2019
Cited alongside, same era.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Nasr, M., Shokri, R., and Houmansadr, A · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference
Sablayrolles, A., Douze, M., Schmid, C., Ollivier, Y., and Jégou, H · 2019
Cited alongside, same era.
Membership inference attacks and defenses in classification models
Li, J., Li, N., and Ribeiro, B · 2021
Later among the works it cites.
Membership leakage in label-only exposures
Li, Z. and Zhang, Y · 2021
Later among the works it cites.
Quantifying the privacy risks of learning high-dimensional graphical models
Murakonda, S. K., Shokri, R., and Theodorakopoulos, G · 2021
Later among the works it cites.
Adversary instantiation: Lower bounds for differentially private machine learning
Nasr, M., Song, S., Thakurta, A., Papernot, N., and Carlini, N · 2021
Later among the works it cites.
Systematic evaluation of privacy risks of machine learning models
Song, L. and Mittal, P · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
Salem, A., Zhang, Y., Humbert, M., Fritz, M., and Backes, M · 2019
Cited alongside, same era.
Demystifying membership inference attacks in machine learning as a service
Truex, S., Liu, L., Gursoy, M. E., Yu, L., and Wei, W · 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.
Membership inference attacks on sequence-to-sequence models: Is my data in your machine translation system?
Hisamoto, S., Post, M., and Duh, K · 2020
Cited alongside, same era.
Revisiting membership inference under realistic assumptions
Jayaraman, B., Wang, L., Knipmeyer, K., Gu, Q., and Evans, D · 2020
Cited alongside, same era.
Stolen memories: Leveraging model memorization for calibrated white-box membership inference
Leino, K. and Fredrikson, M · 2020
Cited alongside, same era.
A pragmatic approach to membership inferences on machine learning models
Long, Y., Wang, L., Bu, D., Bindschaedler, V., Wang, X., Tang, H., Gunter, C. A., and Chen, K · 2020
Cited alongside, same era.
Zhang, C., Ippolito, D., Lee, K., Jagielski, M., Tramèr, F., and Carlini, N · 2021
Later among the works it cites.
Membership inference attacks from first principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., and Tramer, F · 2022
Later among the works it cites.
Relaxloss: Defending membership inference attacks without losing utility
Chen, D., Yu, N., and Fritz, M · 2022
Later among the works it cites.
Auditing membership leakages of multi-exit networks
Li, Z., Liu, Y., He, X., Yu, N., Backes, M., and Zhang, Y · 2022
Later among the works it cites.
Mitigating membership inference attacks by self-distillation through a novel ensemble architecture
Tang, X., Mahloujifar, S., Song, L., Shejwalkar, V., Nasr, M., Houmansadr, A., and Mittal, P · 2022
Later among the works it cites.
Thudi, A., Shumailov, I., Boenisch, F., and Papernot, N · 2022
Later among the works it cites.
Enhanced membership inference attacks against machine learning models
Ye, J., Maddi, A., Murakonda, S. K., Bindschaedler, V., and Shokri, R · 2022
Later among the works it cites.
Scalable membership inference attacks via quantile regression
Bertran, M., Tang, S., Kearns, M., Morgenstern, J., Roth, A., and Wu, Z. S · 2023
Closest in time.
Gaussian membership inference privacy
Leemann, T., Pawelczyk, M., and Kasneci, G · 2023
Closest in time.
Privacy auditing with one (1) training run
Steinke, T., Nasr, M., and Jagielski, M · 2023
Closest in time.
Canary in a coalmine: Better membership inference with ensembled adversarial queries
Wen, Y., Bansal, A., Kazemi, H., Borgnia, E., Goldblum, M., Geiping, J., and Goldstein, T · 2023
Closest in time.
Leave-one-out distinguishability in machine learning
Ye, J., Borovykh, A., Hayou, S., and Shokri, R · 2024
Closest in time.
Membership inference attacks by exploiting loss trajectory
Liu, Y., Zhao, Z., Backes, M., and Yang, Z · 2098
Closest in time.