Machine Learning Models that Remember Too Much
Open accessAbstract
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data.
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Membership inference is the most well-established privacy attack and arguably constitutes a bigger privacy threat than class-level attacks, such as the attacks in [7, 59] and model inversion [18, 19].
“We emphasize that membership inference is the most wellestablished privacy attack and arguably constitutes a bigger privacy threat [13,28,30,37,46,48,60,66,67,75] than classlevel attacks, such as the attacks in [7, 59] and model inversion [18, 19].”
From When Machine Unlearning Jeopardizes Privacy (2021) · cites this paper as [66]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works
Braun, W. J., & Murdoch, D. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993456.011 · Cited by 14,099 · see citing works
Nocedal, J. M., & Wright, S. J. (2006) · Springer series in operations research/Springer series in operations research and financial engineering · doi:10.1007/978-0-387-40065-5 · Cited by 9,575 · see citing works
(2010) · Cited by 8,261 · see citing works
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,134 · 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
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works
Self-citationHan, S., Mao, H., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1510.00149 · Cited by 4,662 · see citing works
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445 · see citing works
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_32 · Cited by 4,408 · see citing works
Learning Word Vectors for Sentiment Analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,710 · see citing works
A maximum entropy approach to natural language processing
Berger, A. L., Pietra, V. J. D., & Pietra, S. A. D. (1996) · doi:10.5555/234285.234289 · Cited by 3,083 · see citing works
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 · see citing works
Self-citationUnderstanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016) · arXiv · doi:10.48550/arxiv.1611.03530 · Cited by 2,838 · see citing works
Best practices for convolutional neural networks applied to visual document analysis
Simard, P., Steinkraus, D., & Platt, J. C. (2005) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2003.1227801 · Cited by 2,805 · 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
Self-citationPang, B., & Lee, L. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219855 · Cited by 2,153 · see citing works
NewsWeeder: Learning to Filter Netnews
Lang, K. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50048-7 · Cited by 2,119 · see citing works
Buciluǎ, C., Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150464 · Cited by 2,068 · see citing works
HMAC: Keyed-Hashing for Message Authentication
Krawczyk, H., Bellare, M., & Canetti, R. (1997) · doi:10.17487/rfc2104 · Cited by 2,035 · see citing works
Static Analysis of Shape in TensorFlow ProgramsPreprint
The Theano Development Team, Al‐Rfou, R., Alain, G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,629 · see citing works
Attribute and simile classifiers for face verification
Kumar, N., Berg, A. C., Belhumeur, P. N., & Nayar, S. K. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459250 · Cited by 1,383 · see citing works
Poisoning Attacks against Support Vector Machines
Biggio, B., Nelson, B., & Laskov, P. (2012) · arXiv · doi:10.48550/arxiv.1206.6389 · Cited by 1,331 · see citing works
Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J. V., Stephan, D. A., Nelson, S. F., & Craig, D. W. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000167 · Cited by 1,221 · see citing works
Dalvi, N. N., Domingos, P. M., Mausam, Sanghai, S. K., & Verma, D. (2004) · Knowledge Discovery and Data Mining · doi:10.1145/1014052.1014066 · Cited by 1,085 · see citing works
Tools for privacy preserving distributed data mining
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/772862.772867 · Cited by 890 · see citing works
Lowd, D., & Meek, C. (2005) · Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining · doi:10.1145/1081870.1081950 · Cited by 755 · see citing works
Machine Learning Classification over Encrypted Data
Bost, R., Popa, R. A., Tu, S., & Goldwasser, S. (2015) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2015.23241 · Cited by 683 · see citing works
Privacy Preserving Data Mining
Lindell, Y., & Pinkas (2002) · Journal of Cryptology · doi:10.1007/s00145-001-0019-2 · Cited by 675 · see citing works
A data-driven approach to cleaning large face datasets
Ng, H., & WINKLER, S. S. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025068 · Cited by 668 · see citing works
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
Fredrikson, M., Lantz, E., Jha, S., Lin, S. M., Page, D., & Ristenpart, T. (2014) · PubMed · Cited by 658 · see citing works
Self-citationVC3: Trustworthy Data Analytics in the Cloud Using SGX
Schuster, F., Costa, M., Cédric Fournet, A., Gkantsidis, C., Peinado, M., Mainar-Ruiz, G., & Russinovich, M. (2015) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2015.10 · Cited by 594 · see citing works
Shielding Applications from an Untrusted Cloud with Haven
Baumann, A., Peinado, M., & Hunt, G. C. (2015) · ACM Transactions on Computer Systems · doi:10.1145/2799647 · Cited by 509 · see citing works
Ateniese, G., Mancini, L. V., Spognardi, A., Villani, A., Vitali, D., & Felici, G. (2015) · International Journal of Security and Networks · doi:10.1504/ijsn.2015.071829 · Cited by 482 · see citing works
SoK: Towards the Science of security and privacy in machine learning
Papernot, N., McDaniel, P. D., Sinha, A., & Wellman, M. P. (2018) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 427 · see citing works
Oblivious multi-party machine learning on trusted processors
Ohrimenko, O., Schuster, F., Cédric Fournet, A., Mehta, A., Nowozin, S., Vaswani, K., & Costa, M. (2016) · MPG.PuRe (Max Planck Society) · Cited by 402 · see citing works
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
Du, W. K., Han, Y. S., & Chen, S. (2004) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.21 · Cited by 376 · see citing works
ANTIDOTE: understanding and defending against poisoning of anomaly detectors
Rubinstein, B. I. P., Nelson, B., Huang, L., Joseph, A. D., Lau, S.-H., Rao, S., Taft, N., & Tygar, J. D. (2009) · Proceedings of the 9th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1644893.1644895 · Cited by 331 · see citing works
Dieleman, S., Schlüter, J., Raffel, C., Olson, E. M., Sønderby, S. K., Nouri, D., Maturana, D., Thoma, M., Battenberg, E., Kelly, J., et al. (2015) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.27878 · Cited by 324 · see citing works
Good Word Attacks on Statistical Spam Filters.
Lowd, D., & Meek, C. (2005) · Cited by 240 · see citing works
Managing security of virtual machine images in a cloud environment
Wei, J., Zhang, X., Ammons, G., et al. (2009) · doi:10.1145/1655008.1655021 · Cited by 239 · see citing works
High-performance secure multi-party computation for data mining applications
Bogdanov, D., Niitsoo, M., Toft, T., et al. (2012) · International Journal of Information Security · doi:10.1007/s10207-012-0177-2 · Cited by 216 · see citing works
Paragraph: Thwarting Signature Learning by Training Maliciously
Newsome, J., Karp, B., & Song, D. X. (2006) · Lecture notes in computer science · doi:10.1007/11856214_5 · Cited by 200 · see citing works
Robust Traceability from Trace Amounts
Dwork, C., Smith, A., Steinke, T., Ullman, J., & Vadhan, S. (2015) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2015.46 · Cited by 142 · see citing works
A security analysis of amazon's elastic compute cloud service
Balduzzi, M., Zaddach, J., Balzarotti, D., et al. (2012) · ACM Symposium on Applied Computing · doi:10.1145/2245276.2232005 · Cited by 137 · see citing works
Membership Privacy in MicroRNA-based Studies
Michael Backes, A., Berrang, P., Humbert, M., & Manoharan, P. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978355 · Cited by 133 · see citing works
Compressing Convolutional Neural Networks in the Frequency Domain
Chen, W., Wilson, J. T., Tyree, S., Weinberger, K. Q., & Chen, Y. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939839 · Cited by 130 · see citing works
Online Anomaly Detection under Adversarial Impact
Kloft, M., & Laskov, P. (2010) · Cited by 118 · see citing works
Bugiel, S., Nürnberger, S., Pöppelmann, T., Sadeghi, A., & Schneider, T. H. (2011) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2046707.2046753 · Cited by 104 · see citing works
Complexity of Word Collocation Networks: A Preliminary Structural Analysis
Lahiri, S. (2014) · doi:10.3115/v1/e14-3011 · Cited by 80 · see citing works
M 2 R: enabling stronger privacy in mapreduce computation
Dinh, T. T. A., Saxena, P., Chang, E., Ooi, B. C., & Zhang, C. (2015) · Figshare · Cited by 80 · see citing works
Zhai, Y., Yin, L., Chase, J. S., et al. (2016) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2987550.2987558 · Cited by 18 · see citing works
Torres-Arias, S., Ammula, A. K., Curtmola, R., & Cappos, J. (2016) · USENIX Security Symposium · Cited by 16 · see citing works
AmazonIA: When Elasticity Snaps Back
Bugiel, S., Nürnberger, S., Pöppelmann, T., et al. (2011) · doi:10.5445/ir/1000084337 · Cited by 11 · see citing works
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Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
Exploiting Unintended Feature Leakage in Collaborative Learning
Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00029 · Cited by 1,734 · see citing works
Self-citationHow to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495 · see citing works
Self-citationPrivacy 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 · see citing works
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610 · see citing works
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106848 · Cited by 581 · see citing works
Ganju, K., Wang, Q., Yang, W., Gunter, C. A., & Borisov, N. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243834 · Cited by 475 · see citing works
LOGAN: Membership Inference Attacks Against Generative Models
Hayes, J., Melis, L., Danezis, G., & De Cristofaro, E. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0008 · Cited by 433 · see citing works
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Golatkar, A., Achille, A., & Soatto, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00932 · Cited by 394 · see citing works
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He, Z., Zhang, T., & Lee, R. B. (2019) · Proceedings of the 35th Annual Computer Security Applications Conference · doi:10.1145/3359789.3359824 · Cited by 313 · see citing works
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Veale, M., Binns, R., & Edwards, L. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0083 · Cited by 278 · see citing works
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Zerka, F., Urovi, V., Vaidyanathan, A., Barakat, S., Leijenaar, R. T. H., Walsh, S., Gabrani-Juma, H., Miraglio, B., Woodruff, H. C., Dumontier, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3029445 · Cited by 89 · see citing works
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Zhou, J., Chen, S., Wu, Y., Li, H., Zhang, B., Zhou, L., Hu, Y., Xiang, Z., Li, Z., Chen, N., et al. (2024) · Science Advances · doi:10.1126/sciadv.adh8601 · Cited by 63 · see citing works
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- Authors
- Song, C., Ristenpart, T., & Shmatikov, V.
- Affiliations
- Cornell University (US)
- Cornell Tech (US)
- Venue
- Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2017-10-30
- Pages
- 587-601
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W2757528734
- Cited by
- 526
- References
- 63
- Citations by year
- 2026: 24 · 2025: 62 · 2024: 83 · 2023: 81 · 2022: 60 · 2021: 84 · 2020: 76 · 2019: 37 · 2018: 17 · 2017: 2
- FWCI
- 16.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1611770, 1704527)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- machine learning privacy, data leakage, training data memorization, sensitive data, personal data protection, privacy-preserving machine learning
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