Privacy Risks of General-Purpose Language Models
Open accessAbstract
Recently, a new paradigm of building general-purpose language models (e.g., Google’s Bert and OpenAI’s GPT-2) in Natural Language Processing (NLP) for text feature extraction, a standard procedure in NLP systems that converts texts to vectors (i.e., embeddings) for downstream modeling, has arisen and starts to find its application in various downstream NLP tasks and real world systems (e.g., Google’s search engine [6]). To obtain general-purpose text embeddings, these language models have highly complicated architectures with millions of learnable parameters and are usually pretrained on billions of sentences before being utilized. As is widely recognized, such a practice indeed improves the state-of-the-art performance of many downstream NLP tasks. However, the improved utility is not for free. We find the text embeddings from general-purpose language models would capture much sensitive information from the plain text. Once being accessed by the adversary, the embeddings can be reverse-engineered to disclose sensitive information of the victims for further harassment. Although such a privacy risk can impose a real threat to the future leverage of these promising NLP tools, there are neither published attacks nor systematic evaluations by far for the mainstream industry-level language models. To bridge this gap, we present the first systematic study on the privacy risks of 8 state-of-the-art language models with 4 diverse case studies. By constructing 2 novel attack classes, our study demonstrates the aforementioned privacy risks do exist and can impose practical threats to the application of general-purpose language models on sensitive data covering identity, genome, healthcare and location. For example, we show the adversary with nearly no prior knowledge can achieve about 75% accuracy when inferring the precise disease site from Bert embeddings of patients’ medical descriptions. As possible countermeasures, we propose 4 different defenses (via rounding, differential privacy, adversarial training and subspace projection) to obfuscate the unprotected embeddings for mitigation purpose. With extensive evaluations, we also provide a preliminary analysis on the utility-privacy trade-off brought by each defense, which we hope may foster future mitigation researches.
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · 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
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works
(2017) · Cited by 11,625 · see citing works
Term-Weighting Approaches in Automatic Text Retrieval
Salton, G., & Buckley, C. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90021-0 · Cited by 11,421 · see citing works
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381 · see citing works
Distributed Representations of Sentences and DocumentsPreprint
Le, Q. V., & Mikolov, T. (2014) · International Conference on Machine Learning · doi:10.48550/arxiv.1405.4053 · Cited by 10,307 · see citing works
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643 · see citing works
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687 · 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
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319 · 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
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856 · see citing works
Speech and language processing
Politt, R., Pollock, J., & Waller, E. (2010) · Taylor & Francis eBooks · doi:10.4324/9780203461891_chapter_3 · Cited by 3,594 · see citing works
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.03771 · Cited by 3,347 · see citing works
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295 · see citing works
Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169 · 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
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Yu, L., Zhang, W., Wang, Y.-X., & Yu, Y. H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10804 · Cited by 2,342 · see citing works
Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906 · 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
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works
Cross-lingual Language Model Pretraining
Lample, G., & Conneau, A. (2019) · arXiv · doi:10.48550/arxiv.1901.07291 · Cited by 1,586 · see citing works
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
Tang, D., Qin, B., & Liu, T. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1167 · Cited by 1,538 · 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
Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015) · arXiv · doi:10.48550/arxiv.1506.06726 · Cited by 1,293 · see citing works
(2016) · International Conference on Machine Learning · Cited by 1,270 · see citing works
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 · see citing works
Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., & Backes, M. (2019) · Proceedings 2019 Network and Distributed System Security Symposium · doi:10.14722/ndss.2019.23119 · Cited by 852 · see citing works
Stealing Machine Learning Models via Prediction APIs
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016) · arXiv · doi:10.48550/arxiv.1609.02943 · Cited by 796 · see citing works
ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding
Sun, Y., Wang, S., Li, Y., Feng, S., Tian, H., Wu, H., & Wang, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6428 · Cited by 766 · see citing works
Shokri, R., Theodorakopoulos, G., Boudec, J. L., & Hubaux, J. (2011) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2011.18 · Cited by 711 · 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
Inverting Visual Representations with Convolutional Networks
Dosovitskiy, A., & Brox, T. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.522 · Cited by 631 · see citing works
Stealing Hyperparameters in Machine Learning
Wang, B., & Gong, N. Z. (2018) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2018.00038 · Cited by 491 · 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
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 · cited 2× · see citing works
“Different from membership inference, another branch of works called property inference aims at infering whether the training set has certain global property, which was first studied by [10] on shallow models and later extended by [23] to deep models.”
BackgroundMachine Learning with Membership Privacy using Adversarial Regularization
Nasr, M., Shokri, R., & Houmansadr, A. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243855 · Cited by 458 · see citing works
Domain-Adversarial Neural NetworksPreprint
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., & Marchand, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.4446 · Cited by 262 · see citing works
Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment
Yang, Z., Zhang, J., Chang, E., & Liang, Z. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3354261 · Cited by 246 · see citing works
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
Song, L., Shokri, R., & Mittal, P. (2019) · Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3319535.3354211 · Cited by 229 · see citing works
Large-Scale Multi-Label Text Classification on EU Legislation
Chalkidis, I., Fergadiotis, E., Malakasiotis, P., & Androutsopoulos, I. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1636 · Cited by 213 · see citing works
Privacy risks from genomic data-sharing beacons
Shringarpure, S. S., & Bustamante, C. D. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.09.010 · Cited by 211 · see citing works
Jochems, A., Deist, T. M., van Soest, J., Eble, M. J., Bulens, P., Coucke, P. A., Dries, W., Lambin, P., & Dekker, A. L. A. J. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.10.002 · Cited by 180 · see citing works
Model-Reuse Attacks on Deep Learning Systems
Ji, Y., Zhang, X., Ji, S., Luo, X., & Wang, T. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243757 · Cited by 170 · see citing works
Self-citationEnd-to-End Deep Image Reconstruction From Human Brain Activity
Shen, G., Dwivedi, K., Majima, K., Horikawa, T., & Kamitani, Y. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00021 · Cited by 163 · see citing works
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Salem, A., Bhattacharya, A., Backes, M., Fritz, M., & Zhang, Y. (2020) · Universität des Saarlandes · doi:10.22028/d291-47370 · Cited by 157 · see citing works
Jochems, A., Deist, T. M., Naqa, I. E., Kessler, M. L., Mayo, C., Reeves, J., Jolly, S., Matuszak, M. M., Haken, R. K. T., van Soest, J., et al. (2017) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2017.04.021 · Cited by 148 · see citing works
Addressing the concerns of the lacks family
Humbert, M., Ayday, E., Hubaux, J., & Telenti, A. (2013) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2508859.2516707 · Cited by 132 · see citing works
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine LearningPreprint
Jia, J., & Gong, N. Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.04810 · Cited by 119 · see citing works
Stealing Neural Networks via Timing Side ChannelsPreprint
Duddu, V., Samanta, D., Rao, D. V., & Bălaş, V. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.11720 · Cited by 115 · see citing works
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 · see citing works
A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation
Shetty, R., Schiele, B., & Fritz, M. (2018) · Max Planck Digital Library · doi:10.60882/cispa.24612720 · Cited by 61 · see citing works
Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach
Zhang, Y., Liu, X., MacLeod, J. N., & Liu, J. (2018) · BMC Genomics · doi:10.1186/s12864-018-5350-1 · Cited by 43 · see citing works
On the Privacy Implications of Location Semantics
Ağır, B., Huguenin, K., Hengartner, U., & Hubaux, J. (2016) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2016-0034 · Cited by 42 · see citing works
DNA-Level Splice Junction Prediction using Deep Recurrent Neural NetworksPreprint
Lee, B., Lee, T., Na, B., & Yoon, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.05135 · Cited by 37 · see citing works
Quantifying Interdependent Risks in Genomic Privacy
Humbert, M., Ayday, E., Hubaux, J., & Telenti, A. (2017) · ACM Transactions on Privacy and Security · doi:10.1145/3035538 · Cited by 37 · see citing works
Zhang, Y., Humbert, M., Rahman, T., Li, C., Pang, J., & Michael Backes, A. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186095 · Cited by 35 · see citing works
A Privacy-Preserving Online Medical Prediagnosis Scheme for Cloud Environment
Guo, W., Shao, J., Lu, R., Liu, Y., & Ghorbani, A. A. (2018) · IEEE Access · doi:10.1109/access.2018.2866971 · Cited by 30 · see citing works
Dissecting Privacy Risks in Biomedical Data
Berrang, P., Humbert, M., Zhang, Y., Lehmann, I., Eils, R., & Michael Backes, A. (2018) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp.2018.00013 · Cited by 20 · see citing works
HS3D: Homo Sapiens Splice Site Data Set
POLLASTRO, P., & Rampone, S. (2003) · Cited by 17 · see citing works
Splice Site Prediction Using Artificial Neural Networks
Johansen, Y. B., Ryen, T., Eftes∅l, T., Kjosmoen, T., & Ruoff, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02504-4_9 · Cited by 11 · see citing works
The Hidden Vulnerability of Distributed Learning in Byzantium
Mhamdi, E. M. E., Guerraoui, R., & Rouault, S. (2018) · arXiv · doi:10.48550/arxiv.1802.07927
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
9 of the 86 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016, http://www.deeplearningbook.org.
- N. Japkowicz and S. Stephen, "The class imbalance problem: A system- atic study," Intell. Data Anal., vol. 6, pp. 429-449, 2002.
Cited by
Sorted by citations
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 · see citing works
Extracting Training Data from Large Language Models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727 · see citing works
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548 · see citing works
Lyu, L., Yu, H., Zhao, J., & Yang, Q. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_1 · Cited by 444 · see citing works
Security and Privacy Challenges of Large Language Models: A Survey
Das, B. C., Amini, M. H., & Wu, Y. (2025) · ACM Computing Surveys · doi:10.1145/3712001 · Cited by 445 · see citing works
A Survey of Privacy Attacks in Machine Learning
Rigaki, M., & Garcia, S. (2024) · ACM Computing Surveys · doi:10.1145/3624010 · Cited by 285 · see citing works
A Survey of Knowledge Tracing: Models, Variants, and Applications
Shen, S., Liu, Q., Huang, Z., Zheng, Y., Yin, M., Wang, M., & Chen, E. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3383325 · Cited by 310 · see citing works
Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
Liu, P., Xu, X., & Wang, W. (2022) · Cybersecurity · doi:10.1186/s42400-021-00105-6 · Cited by 261 · see citing works
Multi-step Jailbreaking Privacy Attacks on ChatGPT
Li, H., Guo, D., Fan, W., Xu, M., Huang, J., Meng, F., & Song, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.272 · Cited by 269 · see citing works
Taking AI risks seriously: a new assessment model for the AI Act
Novelli, C., Casolari, F., Rotolo, A., Taddeo, M., & Floridi, L. (2024) · AI & SOCIETY · doi:10.1007/s00146-023-01723-z · Cited by 139 · see citing works
On Protecting the Data Privacy of Large Language Models (LLMs): A Survey
Yan, B., Li, K., Xu, M., Dong, Y., Zhang, Y., Ren, Z., & Cheng, X. (2024) · International Conference on Meta Computing (ICMC) · doi:10.1109/icmc60390.2024.00008 · Cited by 187 · see citing works
Quantifying Privacy Leakage in Graph Embedding
Duddu, V., Boutet, A., & Shejwalkar, V. (2020) · EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) · doi:10.1145/3448891.3448939 · Cited by 124 · see citing works
Are Large Pre-Trained Language Models Leaking Your Personal Information?
Huang, J., Shao, H., & Chang, K. C. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.148 · Cited by 135 · see citing works
The Security of Using Large Language Models: A Survey with Emphasis on ChatGPT
Zhou, W., Zhu, X., Han, Q., Li, L., Chen, X., Wen, S., & Xiang, Y. (2024) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2024.124983 · Cited by 101 · see citing works
Zhang, Z., Jia, M., Lee, H.-P. (., Yao, B., Das, S., Lerner, A., Wang, D., & Li, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642385 · Cited by 95 · see citing works
“Some topics are inherently more sensitive, such as the Financial and Medical topics [15, 57, 77], which naturally lend themselves to users’ sharing information like their transaction histories and medical diagnoses, respectively.”
BackgroundAn overview of large AI models and their applications
Tu, X., He, Z., Huang, Y., Zhang, Z., Yang, M., & Zhao, J. C. (2024) · Visual Intelligence · doi:10.1007/s44267-024-00065-8 · Cited by 95 · see citing works
Self-citationFraile Navarro, D., Ijaz, K., Rezazadegan, D., Rahimi-Ardabili, H., Dras, M., Coiera, E., & Berkovsky, S. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105122 · Cited by 82 · see citing works
On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review
Yan, B., Li, K., Xu, M., Dong, Y., Zhang, Y., Ren, Z., & Cheng, X. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100300 · Cited by 93 · see citing works
Natural language processing in the era of large language models
Zubiaga, A. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1350306 · Cited by 81 · see citing works
Backdoor Pre-trained Models Can Transfer to All
Shen, L., Ji, S., Zhang, X., Li, J., Chen, J., Shi, J., Fang, C., Yin, J., & Wang, T. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3485370 · Cited by 87 · see citing works
Self-citationWang, Y., Pan, Y., Su, Z., Deng, Y., Zhao, Q., Du, L., Luan, T. H., Kang, J., & Niyato, D. T. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3576176 · Cited by 86 · see citing works
Integration of large language models and federated learning
Chen, C., Feng, X., Li, Y., Lyu, L., Zhou, J., Zheng, X., & Yin, J. (2024) · Patterns · doi:10.1016/j.patter.2024.101098 · Cited by 103 · see citing works
Sousa, S., & Kern, R. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10204-6 · Cited by 74 · see citing works
Toward Federated Large Language Models: Motivations, Methods, and Future Directions
Cheng, Y., Zhang, W., Zhang, Z., Zhang, C., Wang, S., & Mao, S. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3503680 · Cited by 65 · see citing works
Deep learning model inversion attacks and defenses: a comprehensive survey
Yang, W., Wang, S., Wu, D., Cai, T., Zhu, Y., Wei, S., Zhang, Y., Yang, X., Tang, Z., & Li, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11248-0 · Cited by 71 · see citing works
The Importance of AI Data Governance in Large Language Models
(2025) · doi:10.3390/bdcc9060147 · Cited by 58 · see citing works
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass
Du, M., Yue, X., Chow, S. S. M., Wang, T., Huang, C., & Sun, H. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3616592 · Cited by 63 · see citing works
Differential Privacy for Text Analytics via Natural Text Sanitization
Yue, X., Du, M., Wang, T., Li, Y., Sun, H., & Chow, S. S. M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.337 · Cited by 65 · see citing works
Foundation Models in Agriculture: A Comprehensive Review
Yin, S., Xi, Y., Zhang, X., Sun, C., & Mao, Q. (2025) · Agriculture · doi:10.3390/agriculture15080847 · Cited by 60 · see citing works
Dong, Y., Jiang, X., Liu, H., Jin, Z., Gu, B., Yang, M., & Li, G. (2024) · arXiv · doi:10.48550/arxiv.2402.15938 · Cited by 52
“Some work (Pan et al., 2020; Zhou et al., 2023; Jacovi et al., 2023; Dodge et al., 2021) exposed the serious consequences of data contamination and urged attention to this problem.”
Self-citationNatural Language Understanding with Privacy-Preserving BERT
Qu, C., Kong, W., Yang, L., Zhang, M., Bendersky, M., & Najork, M. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482281 · Cited by 56 · see citing works
Self-citationTextHide: Tackling Data Privacy in Language Understanding Tasks
Huang, Y., Song, Z., Chen, D., Li, K., & Arora, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.123 · Cited by 53 · see citing works
A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models
Derner, E., Batistič, K., Zahálka, J., & Babuška, R. (2024) · IEEE Access · doi:10.1109/access.2024.3450388 · Cited by 58 · see citing works
Treating medical data as a durable asset
Telenti, A., & Jiang, X. (2020) · Nature Genetics · doi:10.1038/s41588-020-0698-y · Cited by 46 · see citing works
A comprehensive review on deep learning algorithms: Security and privacy issues
Tayyab, M. A., Marjani, M., Jhanjhi, N. Z., Hashem, M., Usmani, R. S. A., & Qamar, F. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103297 · Cited by 45 · see citing works
LLM-PBE: Assessing Data Privacy in Large Language Models
Li, Q., Hong, J., Xie, C., Tan, J., Xin, R., Hou, J., Yin, X., Wang, Z., Hendrycks, D., Wang, Z., et al. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3681954.3681994 · Cited by 42 · see citing works
Berge, G. T., Granmo, O., Tveit, T. O., Ruthjersen, A. L., & Sharma, J. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02271-8 · Cited by 44 · see citing works
Chen, Y., Gao, H., Cui, G., Qi, F., Huang, L., Liu, Z., & Sun, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.771 · Cited by 53 · see citing works
Review: Privacy-Preservation in the Context of Natural Language Processing
Mahendran, D., Luo, C., & McInnes, B. T. (2021) · IEEE Access · doi:10.1109/access.2021.3124163 · Cited by 37 · see citing works
Artificial intelligence: Augmenting telehealth with large language models
Snoswell, C. L., Snoswell, A., Kelly, J. T., Caffery, L. J., & Smith, A. C. (2023) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x231169055 · Cited by 37 · see citing works
Li, H., Xu, M., & Song, Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.881 · Cited by 34 · see citing works
Quantifying and Mitigating Privacy Risks of Contrastive Learning
He, X., & Zhang, Y. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484571 · Cited by 38 · see citing works
Abedi, M., Alshybani, I., Shahadat, M., & Murillo, M. S. (2023) · Qeios · doi:10.32388/md04b0 · Cited by 41 · see citing works
Fraile Navarro, D., Kocaballi, A. B., Dras, M., & Berkovsky, S. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3569893 · Cited by 28 · see citing works
Practical Attribute Reconstruction Attack Against Federated Learning
Chen, C., Lyu, L., Yu, H., & Chen, G. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3159236 · Cited by 30 · see citing works
Olszewski, D., Lu, A., Stillman, C., Warren, K., Kitroser, C., Pascual, A., Ukirde, D., Butler, K. R. B., & Traynor, P. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623130 · Cited by 33 · see citing works
PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification
Yao, H., Lou, J., Qin, Z., & Ren, K. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00209 · Cited by 29 · see citing works
Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks
Zhang, Z., Sun, Y., Wang, Z., Nie, Y., Ma, X., Li, R., Sun, P., & Ban, X. J. (2025) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981251367699 · Cited by 28 · see citing works
Differential Privacy in Natural Language Processing The Story So Far
Klymenko, A., Meisenbacher, S., & Matthes, F. (2022) · Workshop on Privacy in Natural Language Processing (PrivateNLP) · doi:10.18653/v1/2022.privatenlp-1.1 · Cited by 29 · see citing works
Gradient leakage attacks in federated learning
Gong, H., Jiang, L., Liu, X., Wang, Y., Gastro, O., Wang, L., Zhang, K., & Guo, Z. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10550-z · Cited by 27 · see citing works
LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust
Zeng, W., Alrahis, L., Mankali, L., Knechtel, J., & Sinanoglu, O. (2024) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi61997.2024.00076 · Cited by 25 · see citing works
A Review of Large Language Models in Healthcare: Taxonomy, Threats, Vulnerabilities, and Framework
Hamid, R., & Brohi, S. N. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8110161 · Cited by 25 · see citing works
Artificial intelligence for global health: cautious optimism with safeguards
Pujari, S., Reis, A. A., Zhao, Y., Alsalamah, S., Serhan, F., Reeder, J. C., & Labrique, A. B. (2023) · Bulletin of the World Health Organization · doi:10.2471/blt.23.290215 · Cited by 24 · see citing works
Privacy Implications of Retrieval-Based Language Models
Huang, Y., Gupta, S., Zhong, Z., Li, K., & Chen, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.921 · Cited by 27 · see citing works
Zhou, X., Lu, Y., Ma, R., Gui, T., Wang, Y., Ding, Y., Zhang, Y., Zhang, Q., & Huang, X. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.337 · Cited by 21 · see citing works
Membership Inference Attacks Against Deep Learning Models via Logits Distribution
Yan, H., Li, S., Wang, Y., Zhang, Y., Sharif, K., Hu, H., & Li, Y. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3222880 · Cited by 21 · see citing works
Electronic Health Records as Source of Research Data
Wang, W., Ferrari, D., Haddon-Hill, G. W., & Ćurčin, V. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_11 · Cited by 26 · see citing works
The “Code” of Ethics: A Holistic Audit of AI Code Generators
Ma, W., Song, Y., Xue, M., Wen, S., & Xiang, Y. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3367737 · Cited by 21 · see citing works
Artificial intelligence security and privacy: a survey
He, X., Xu, G., Han, X., Wang, Q., Zhao, L., Shen, C., Lin, C., Zhao, Z., Li, Q., Yang, L., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-025-4388-5 · Cited by 23 · see citing works
Self-citationAbedi, M., Alshybani, I., Shahadat, M., & Murillo, M. S. (2023) · Qeios · doi:10.32388/md04b0.2 · Cited by 20 · see citing works
Sanitizing Sentence Embeddings (and Labels) for Local Differential Privacy
Du, M., Yue, X., Chow, S. S. M., & Sun, H. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583512 · Cited by 19 · see citing works
Sentence-level Privacy for Document Embeddings
Meehan, C., Mrini, K., & Chaudhuri, K. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.238 · Cited by 17 · see citing works
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
Zou, W., Geng, R., Wang, B., & Jia, J. (2024) · arXiv · doi:10.48550/arxiv.2402.07867 · Cited by 16 · see citing works
A comprehensive review of current trends, challenges, and opportunities in text data privacy
Shahriar, S., Dara, R. A., & Akalu, R. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104358 · Cited by 17 · see citing works
Li, H., Song, Y., & Fan, L. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.429 · Cited by 15 · see citing works
Towards Sentence Level Inference Attack Against Pre-trained Language Models
Gu, K., Kabir, E., Ramsurrun, N., Vosoughi, S., & Mehnaz, S. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0070 · Cited by 15 · see citing works
Large language models transform biological research: from architecture to utilization
Wang, T., & Luo, Z. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4466-3 · Cited by 14 · see citing works
TextFusion: Privacy-Preserving Pre-trained Model Inference via Token Fusion
Zhou, X., Lu, J., Gui, T., Ma, R., Fei, Z., Wang, Y., Ding, Y., Cheung, Y., Zhang, Q., & Huang, X. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.572 · Cited by 14 · see citing works
Wang, F., Lin, M., Ma, Y., Liu, H., He, Q., Tang, X., Tang, J., Pei, J., & Wang, S. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736563 · Cited by 16 · see citing works
Lee, T.-H., Kim, S., Lee, J., & Jun, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119728 · Cited by 16 · see citing works
Watermarking techniques for large language models: a survey
Liang, Y., Xiao, J., Gan, W., & Yu, P. S. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-025-11474-6 · Cited by 26 · see citing works
Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey
Sakib, M. N., Islam, M. A., Pathak, R., & Arifin, M. M. (2024) · International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings) · doi:10.1109/aibthings63359.2024.10863356 · Cited by 13 · see citing works
Prompt Inversion Attack Against Collaborative Inference of Large Language Models
Qu, W., Zhou, Y., Wu, Y., Xiao, T., Yuan, B., Li, Y., & Zhang, J. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00160 · Cited by 11 · see citing works
Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models
Munir, S., Batool, B., Shafiq, Z., Srinivasan, P., & Zaffar, F. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.155 · Cited by 11 · see citing works
Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models
Laufer, B., Kleinberg, J. M., & Heidari, H. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645366 · Cited by 10 · see citing works
Large language models reshaping molecular biology and drug development
Tripathi, S., Gabriel, K., Tripathi, P. K., & Kim, E. (2024) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14568 · Cited by 13 · see citing works
Differentially Private Recurrent Variational Autoencoder For Text Privacy Preservation
Wang, Y., Meng, X., & Liu, X. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02096-9 · Cited by 10 · see citing works
Matrix Gaussian Mechanisms for Differentially-Private Learning
Yang, J., Xiang, L., Yu, J., Wang, X., Guo, B., Li, Z., & Li, B. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3093316 · Cited by 10 · see citing works
Recover User’s Private Training Image Data by Gradient in Federated Learning
Gong, H., Jiang, L., Liu, X., Wang, Y., Wang, L., & Zhang, K. (2022) · Sensors · doi:10.3390/s22197157 · Cited by 10 · see citing works
Automatic Generation of Adversarial Readable Chinese Texts
Liu, M., Zhang, Z., Zhang, Y., Zhang, C., Li, Z., Li, Q. L., Duan, H., & Sun, D. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3164289 · Cited by 9 · see citing works
Choi, D., Choi, S., Lee, E., Seo, J., & Na, D. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00014 · Cited by 9 · see citing works
A General Framework for Data-Use Auditing of ML Models
Huang, Z., Gong, N. Z., & Reiter, M. K. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690226 · Cited by 9 · see citing works
Privacy-Preserving Models for Legal Natural Language Processing
Yin, Y., & Habernal, I. (2022) · Natural Legal Language Processing Workshop (NLLP) · doi:10.18653/v1/2022.nllp-1.14 · Cited by 11 · see citing works
A survey of joint security-safety for function, information and human in industry 5.0
Hong, Y., Wu, J., & Guan, X. (2024) · Security and Safety · doi:10.1051/sands/2024014 · Cited by 11 · see citing works
Palanisamy, B., Chakrabarti, A., Singh, A., Hassija, V., Chalapathi, G., & Singh, A. R. (2024) · IEEE Access · doi:10.1109/access.2024.3521596 · Cited by 10 · see citing works
Alamsyah, N., Haq, M. A., & Susanto, C. O. N. (2024) · International Conference on Information Technology and Computing (ICITCOM) · doi:10.1109/icitcom62788.2024.10762118 · Cited by 8 · see citing works
Privacy Issues in Large Language Models: A SurveyPreprint
Khan, W. Z., Kibriya, H., Siddiqa, A., & Khan, M. K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4871294 · Cited by 8 · see citing works
ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in MedicinePreprint
Johnson, R., Gottlieb, U., Shaham, G., Eisen, L., Waxman, J. G., Devons-Sberro, S., Ginder, C. R., Hong, P. J., Sayeed, R., Su, X., et al. (2024) · medRxiv · doi:10.1101/2024.12.03.24318322 · Cited by 8 · see citing works
Pharmacist vs machine: Pharmacy services in the age of large language models
Snoswell, C. L., Falconer, N., & Snoswell, A. (2023) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2023.03.006 · Cited by 7 · see citing works
Guiding Text-to-Text Privatization by Syntax
Arnold, S., Yesilbas, D., & Weinzierl, S. (2023) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2023.trustnlp-1.14 · Cited by 6 · see citing works
Provable trade-offs between private & robust machine learning.Preprint
Hayes, J. (2020) · arXiv (Cornell University) · Cited by 6 · see citing works
VFLAIR-LLM: A Comprehensive Framework and Benchmark for Split Learning of LLMs
Gu, Z., Fan, Q., Sun, L., Liu, Y., & Ye, X. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737411 · Cited by 6 · see citing works
Anonymization-as-a-Service: The Service Center Transcripts Industrial Case
Borovits, N., Bardelloni, G., Tamburri, D. A., & van den Heuvel, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48424-7_19 · Cited by 5 · see citing works
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems
Perez, I., Wong, J., Skalski, P., Burrell, S. A., Mortier, R., McAuley, D., & Sutton, D. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00068 · Cited by 6 · see citing works
Contrast-Then-Approximate: Analyzing Keyword Leakage of Generative Language Models
Zeng, Z., Xiang, T., Guo, S., He, J., Zhang, Q., Xu, G., & Zhang, T. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3392535 · Cited by 4 · see citing works
On-premise AI agents: a future foundation for education, academia, and industry
Yin, H. (2026) · AI Agent · doi:10.20517/aiagent.2025.11 · Cited by 6 · see citing works
Xu, H., Zhang, Z., Yu, X., Wu, Y., Zha, Z., Xu, B., Xu, W., Hu, M., & Peng, K. (2024) · Applied Sciences · doi:10.3390/app14167118 · Cited by 4 · see citing works
DOME: Directional medical embedding vectors from Electronic Health Records
Wen, J., Xue, H., Rush, E. N., Panickan, V. A., Cai, T. A., Zhou, D., Zhou, D., Costa, L., Begoli, E., Hong, C., et al. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104768 · Cited by 5 · see citing works
Matryoshka: Exploiting the Over-Parametrization of Deep Learning Models for Covert Data Transmission
Pan, X., Zhang, M., Yan, Y., Zhang, S., & Yang, M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3434417 · Cited by 4 · see citing works
Self-citationPrivacy-preserving revocable access control for LLM-driven electrical distributed systems
Xiao, P., Yang, S., Wang, H., Zhang, Z., & Zou, C. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-01964-3 · Cited by 4 · see citing works
- Authors
- Pan, X., Zhang, M., Ji, S., & Yang, M.
- Affiliations
- Fudan University (CN)
- Zhejiang University (CN)
- Venue
- 2020 IEEE Symposium on Security and Privacy (SP)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 1314-1331
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W3046764764
- Cited by
- 188
- References
- 86
- Citations by year
- 2026: 20 · 2025: 50 · 2024: 43 · 2023: 36 · 2022: 21 · 2021: 12 · 2020: 6
- FWCI
- 7.16
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Symposium on Security and Privacy (S&P) (publishedVersion, public-domain)
- Funders
- National Natural Science Foundation of China (61772466, U1936215, and U1836202, U1936215, U1836210, U1636204, 61972099, U1836213, U1836202, 61772466, U1736208, 2018YFB0804102)
- Ant Financial Services Group
- National Key Research and Development Program of China (2018YFB0804102)
- Natural Science Foundation of Zhejiang Province (LR19F020003, U1936215)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- privacy risks, text embeddings, sensitive information disclosure, BERT, GPT-2, differential privacy, adversarial training, subspace projection, privacy-utility trade-off
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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