Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
  • Das B, Amini M and Wu Y. (2025). Security and Privacy Challenges of Large Language Models: A Survey. ACM Computing Surveys. 57:6. (1-39). Online publication date: 30-Jun-2025.

    https://doi.org/10.1145/3712001

  • Liang K, Li S, Ding M, Tian F and Wu Y. Privacy-Preserving Coded Schemes for Multi-Server Federated Learning With Straggling Links. IEEE Transactions on Information Forensics and Security. 10.1109/TIFS.2024.3524160. 20. (1222-1236).

    https://ieeexplore.ieee.org/document/10818498/

  • Shahriar S, Dara R and Akalu R. (2025). A Comprehensive Review of Current Trends, Challenges, and Opportunities in Text Data Privacy. Computers & Security. 10.1016/j.cose.2025.104358. (104358). Online publication date: 1-Jan-2025.

    https://linkinghub.elsevier.com/retrieve/pii/S0167404825000471

  • Goto Y, Ashizawa N, Shibahara T and Yanai N. (2025). Do Backdoors Assist Membership Inference Attacks?. Security and Privacy in Communication Networks. 10.1007/978-3-031-64954-7_13. (251-265).

    https://link.springer.com/10.1007/978-3-031-64954-7_13

  • Zhang J, Zhou W and Ujcich B. (2024). Provenance-Enabled Explainable AI. Proceedings of the ACM on Management of Data. 2:6. (1-27). Online publication date: 18-Dec-2024.

    https://doi.org/10.1145/3698826

  • Liu Y, Huang J, Li Y, Wang D and Xiao B. (2024). Generative AI model privacy: a survey. Artificial Intelligence Review. 10.1007/s10462-024-11024-6. 58:1.

    https://link.springer.com/10.1007/s10462-024-11024-6

  • He Y, Li B, Wang Y, Yang M, Wang J, Hu H and Zhao X. Is Difficulty Calibration All We Need? Towards More Practical Membership Inference Attacks. Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security. (1226-1240).

    https://doi.org/10.1145/3658644.3690316

  • Guan F, Zhu T, Tong H and Zhou W. (2024). Topology modification against membership inference attack in Graph Neural Networks. Knowledge-Based Systems. 10.1016/j.knosys.2024.112642. 305. (112642). Online publication date: 1-Dec-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S0950705124012760

  • Ullah I, Hassan N, Gill S, Suleiman B, Ahanger T, Shah Z, Qadir J and Kanhere S. (2024). Privacy preserving large language models: ChatGPT case study based vision and framework. IET Blockchain. 10.1049/blc2.12091.

    https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/blc2.12091

  • Mökander J, Schuett J, Kirk H and Floridi L. (2023). Auditing large language models: a three-layered approach. AI and Ethics. 10.1007/s43681-023-00289-2. 4:4. (1085-1115). Online publication date: 1-Nov-2024.

    https://link.springer.com/10.1007/s43681-023-00289-2

  • Mu X, Pang M and Zhu F. Data Provenance via Differential Auditing. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2023.3334821. 36:10. (5066-5079).

    https://ieeexplore.ieee.org/document/10323535/

  • Sankar B, Gilliland D, Rincon J, Hermjakob H, Yan Y, Adam I, Lemaster G, Wang D, Watson K, Bui A, Wang W and Ping P. (2024). Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundation Models. Bioengineering. 10.3390/bioengineering11100984. 11:10. (984).

    https://www.mdpi.com/2306-5354/11/10/984

  • Gao X, Chen J, Wang J, Shi J, Cheng P and Chen J. TeDA: A Testing Framework for Data Usage Auditing in Deep Learning Model Development. Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis. (1479-1490).

    https://doi.org/10.1145/3650212.3680375

  • Cavasin S, Mari D, Milani S and Conti M. (2024). Fingerprint membership and identity inference against generative adversarial networks. Pattern Recognition Letters. 10.1016/j.patrec.2024.07.018. 185. (184-189). Online publication date: 1-Sep-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S0167865524002216

  • Cheng Y, Luo S, Pan L, Wan Y and Li X. (2024). HAMIATCM: high-availability membership inference attack against text classification models under little knowledge. Applied Intelligence. 10.1007/s10489-024-05495-x. 54:17-18. (7994-8019). Online publication date: 1-Sep-2024.

    https://link.springer.com/10.1007/s10489-024-05495-x

  • Meeus M, Jain S, Rei M and de Montjoye Y. Did the neurons read your book? document-level membership inference for large language models. Proceedings of the 33rd USENIX Conference on Security Symposium. (2369-2385).

    /doi/10.5555/3698900.3699033

  • Jiang Z, Ye P, He S, Wang W, Chen R and Li B. Lotto. Proceedings of the 33rd USENIX Conference on Security Symposium. (343-360).

    /doi/10.5555/3698900.3698920

  • Chi X, Zhang X, Wang Y, Qi L, Beheshti A, Xu X, Choo K, Wang S and Hu H. Shadow-free membership inference attacks. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. (5781-5789).

    https://doi.org/10.24963/ijcai.2024/639

  • Meeus M, Shilov I, Faysse M and De Montjoye Y. Copyright traps for large language models. Proceedings of the 41st International Conference on Machine Learning. (35296-35309).

    /doi/10.5555/3692070.3693506

  • Chen B, She B, Hawkins C, Benvenuti A, Fallin B, Paré P and Hale M. (2024). Differentially Private Computation of Basic Reproduction Numbers in Networked Epidemic Models 2024 American Control Conference (ACC). 10.23919/ACC60939.2024.10644264. 979-8-3503-8265-5. (4422-4427).

    https://ieeexplore.ieee.org/document/10644264/

  • Zeng Z, He J, Xiang T, Wang N, Chen B and Guo S. (2024). Cognitive Tracing Data Trails: Auditing Data Provenance in Discriminative Language Models Using Accumulated Discrepancy Score. Cognitive Computation. 10.1007/s12559-024-10315-y.

    https://link.springer.com/10.1007/s12559-024-10315-y

  • Alebouyeh Z and Bidgoly A. (2024). Benchmarking robustness and privacy-preserving methods in federated learning. Future Generation Computer Systems. 10.1016/j.future.2024.01.009. 155. (18-38). Online publication date: 1-Jun-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S0167739X24000086

  • Jayaraman B, Ghosh E, Chase M, Roy S, Dai W and Evans D. (2024). Combing for Credentials: Active Pattern Extraction from Smart Reply 2024 IEEE Symposium on Security and Privacy (SP). 10.1109/SP54263.2024.00041. 979-8-3503-3130-1. (1443-1461).

    https://ieeexplore.ieee.org/document/10646876/

  • Oruche R, Akula R, Goruganthu S and Calyam P. (2024). Holistic Multi-layered System Design for Human-Centered Dialog Systems 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS). 10.1109/ICHMS59971.2024.10555807. 979-8-3503-1579-0. (1-8).

    https://ieeexplore.ieee.org/document/10555807/

  • Rigaki M and Garcia S. (2023). A Survey of Privacy Attacks in Machine Learning. ACM Computing Surveys. 56:4. (1-34). Online publication date: 30-Apr-2024.

    https://doi.org/10.1145/3624010

  • Vats A, Liu Z, Su P, Paul D, Ma Y, Pang Y, Ahmed Z and Kalinli O. (2024). Recovering from Privacy-Preserving Masking with Large Language Models ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 10.1109/ICASSP48485.2024.10448234. 979-8-3503-4485-1. (10771-10775).

    https://ieeexplore.ieee.org/document/10448234/

  • Liu Z and Kalinli O. (2024). Forgetting Private Textual Sequences in Language Models Via Leave-One-Out Ensemble ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 10.1109/ICASSP48485.2024.10446299. 979-8-3503-4485-1. (10261-10265).

    https://ieeexplore.ieee.org/document/10446299/

  • Niu J, Liu P, Zhu X, Shen K, Wang Y, Chi H, Shen Y, Jiang X, Ma J and Zhang Y. (2024). A survey on membership inference attacks and defenses in Machine Learning. Journal of Information and Intelligence. 10.1016/j.jiixd.2024.02.001. Online publication date: 1-Mar-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S2949715924000064

  • Guan F, Zhu T, Zhou W and Choo K. (2024). Graph neural networks: a survey on the links between privacy and security. Artificial Intelligence Review. 10.1007/s10462-023-10656-4. 57:2.

    https://link.springer.com/10.1007/s10462-023-10656-4

  • Zeng Z, Xiang T, Guo S, He J, Zhang Q, Xu G and Zhang T. Contrast-Then-Approximate: Analyzing Keyword Leakage of Generative Language Models. IEEE Transactions on Information Forensics and Security. 10.1109/TIFS.2024.3392535. 19. (5166-5180).

    https://ieeexplore.ieee.org/document/10506703/

  • Ma W, Song Y, Xue M, Wen S and Xiang Y. The “Code” of Ethics: A Holistic Audit of AI Code Generators. IEEE Transactions on Dependable and Secure Computing. 10.1109/TDSC.2024.3367737. (1-16).

    https://ieeexplore.ieee.org/document/10440501/

  • Duddu V, Das A, Khayata N, Yalame H, Schneider T and Asokan N. (2024). Attesting Distributional Properties of Training Data for Machine Learning. Computer Security – ESORICS 2024. 10.1007/978-3-031-70879-4_1. (3-23).

    https://link.springer.com/10.1007/978-3-031-70879-4_1

  • Duan H, Dziedzic A, Papernot N and Boenisch F. Flocks of stochastic parrots. Proceedings of the 37th International Conference on Neural Information Processing Systems. (76852-76871).

    /doi/10.5555/3666122.3669480

  • Kim S, Yun S, Lee H, Gubri M, Yoon S and Oh S. ProPILE. Proceedings of the 37th International Conference on Neural Information Processing Systems. (20750-20762).

    /doi/10.5555/3666122.3667033

  • Wang X and Wang W. Link Membership Inference Attacks against Unsupervised Graph Representation Learning. Proceedings of the 39th Annual Computer Security Applications Conference. (477-491).

    https://doi.org/10.1145/3627106.3627115

  • Miao Y, Yu Y, Li X, Guo Y, Liu X, Choo K and Deng R. Defending Against Membership Inference Attack by Shielding Membership Signals. IEEE Transactions on Services Computing. 10.1109/TSC.2023.3309336. 16:6. (4087-4101).

    https://ieeexplore.ieee.org/document/10233063/

  • El Mestari S, Lenzini G and Demirci H. (2023). Preserving Data Privacy in Machine Learning Systems. Computers & Security. 10.1016/j.cose.2023.103605. (103605). Online publication date: 1-Nov-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S0167404823005151

  • Ko M, Jin M, Wang C and Jia R. (2023). Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study 2023 IEEE/CVF International Conference on Computer Vision (ICCV). 10.1109/ICCV51070.2023.00449. 979-8-3503-0718-4. (4848-4858).

    https://ieeexplore.ieee.org/document/10378100/

  • Chen M, Zhang Z, Wang T, Backes M and Zhang Y. FACE-AUDITOR. Proceedings of the 32nd USENIX Conference on Security Symposium. (7195-7212).

    /doi/10.5555/3620237.3620640

  • Li H and Zhao X. (2023). Membership Information Leakage in Well-Generalized Auto Speech Recognition Systems 2023 International Conference on Data Science and Network Security (ICDSNS). 10.1109/ICDSNS58469.2023.10245166. 979-8-3503-0159-5. (1-7).

    https://ieeexplore.ieee.org/document/10245166/

  • Zanella-Béguelin S, Wutschitz L, Tople S, Salem A, Rühle V, Paverd A, Naseri M, Köpf B and Jones D. Bayesian estimation of differential privacy. Proceedings of the 40th International Conference on Machine Learning. (40624-40636).

    /doi/10.5555/3618408.3620110

  • Choquette-Choo C, McMahan H, Rush K and Thakurta A. Multi-epoch matrix factorization mechanisms for private machine learning. Proceedings of the 40th International Conference on Machine Learning. (5924-5963).

    /doi/10.5555/3618408.3618644

  • Long Y, Ying Z, Yan H, Fang R, Li X, Wang Y and Pan Z. (2023). Membership reconstruction attack in deep neural networks. Information Sciences: an International Journal. 634:C. (27-41). Online publication date: 1-Jul-2023.

    https://doi.org/10.1016/j.ins.2023.03.008

  • Liu Z, Zhang X and Peng F. (2023). Mitigating Unintended Memorization in Language Models Via Alternating Teaching ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 10.1109/ICASSP49357.2023.10096557. 978-1-7281-6327-7. (1-5).

    https://ieeexplore.ieee.org/document/10096557/

  • Matsumoto T, Miura T and Yanai N. (2023). Membership Inference Attacks against Diffusion Models 2023 IEEE Security and Privacy Workshops (SPW). 10.1109/SPW59333.2023.00013. 979-8-3503-1236-2. (77-83).

    https://ieeexplore.ieee.org/document/10188618/

  • Zhu Z, Wu C, Fan R, Lian D and Chen E. Membership Inference Attacks Against Sequential Recommender Systems. Proceedings of the ACM Web Conference 2023. (1208-1219).

    https://doi.org/10.1145/3543507.3583447

  • Jiang Z, Wang W, Li B and Yang Q. Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies. IEEE Transactions on Big Data. 10.1109/TBDATA.2022.3177222. 9:2. (437-454).

    https://ieeexplore.ieee.org/document/9780218/

  • Peris C, Dupuy C, Majmudar J, Parikh R, Smaili S, Zemel R and Gupta R. Privacy in the Time of Language Models. Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. (1291-1292).

    https://doi.org/10.1145/3539597.3575792

  • Solanki P, Grundy J and Hussain W. (2022). Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers. AI and Ethics. 10.1007/s43681-022-00195-z. 3:1. (223-240). Online publication date: 1-Feb-2023.

    https://link.springer.com/10.1007/s43681-022-00195-z

  • Sousa S and Kern R. (2022). How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing. Artificial Intelligence Review. 10.1007/s10462-022-10204-6. 56:2. (1427-1492). Online publication date: 1-Feb-2023.

    https://link.springer.com/10.1007/s10462-022-10204-6

  • Liu Y, Jiang P and Zhu L. Subject-Level Membership Inference Attack via Data Augmentation and Model Discrepancy. IEEE Transactions on Information Forensics and Security. 10.1109/TIFS.2023.3318950. 18. (5848-5859).

    https://ieeexplore.ieee.org/document/10262058/

  • Gomrokchi M, Amin S, Aboutalebi H, Wong A and Precup D. Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning. IEEE Access. 10.1109/ACCESS.2023.3270860. 11. (42796-42808).

    https://ieeexplore.ieee.org/document/10108924/

  • El Mestari S, Doğan F and Maria Botes W. (2023). Technical and Legal Aspects Relating to the (Re)Use of Health Data When Repurposing Machine Learning Models in the EU. Privacy Symposium 2023. 10.1007/978-3-031-44939-0_3. (33-48).

    https://link.springer.com/10.1007/978-3-031-44939-0_3

  • Jia J, Liu H and Gong N. (2023). 10 Security and Privacy Problems in Large Foundation Models. AI Embedded Assurance for Cyber Systems. 10.1007/978-3-031-42637-7_8. (139-159).

    https://link.springer.com/10.1007/978-3-031-42637-7_8

  • Tirumala K, Markosyan A, Zettlemoyer L and Aghajanyan A. Memorization without overfitting. Proceedings of the 36th International Conference on Neural Information Processing Systems. (38274-38290).

    /doi/10.5555/3600270.3603043

  • Song C, Granqvist F and Talwar K. FLAIR. Proceedings of the 36th International Conference on Neural Information Processing Systems. (37792-37805).

    /doi/10.5555/3600270.3603009

  • Tian Z, Zhao Y, Huang Z, Wang Y, Zhang N and He H. SeqPATE. Proceedings of the 36th International Conference on Neural Information Processing Systems. (11117-11130).

    /doi/10.5555/3600270.3601078

  • Denisov S, McMahan H, Rush K, Smith A and Thakurta A. Improved differential privacy for SGD via optimal private linear operators on adaptive streams. Proceedings of the 36th International Conference on Neural Information Processing Systems. (5910-5924).

    /doi/10.5555/3600270.3600698

  • Hu P, Wang Z, Sun R, Wang H and Xue M. M4I. Proceedings of the 36th International Conference on Neural Information Processing Systems. (1867-1882).

    /doi/10.5555/3600270.3600406

  • Conti M, Li J, Picek S and Xu J. Label-Only Membership Inference Attack against Node-Level Graph Neural Networks. Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security. (1-12).

    https://doi.org/10.1145/3560830.3563734

  • Miao Y, Chen C, Pan L, Liu S, Camtepe S, Zhang J and Xiang Y. No-Label User-Level Membership Inference for ASR Model Auditing. Computer Security – ESORICS 2022. (610-628).

    https://doi.org/10.1007/978-3-031-17146-8_30

  • Lu Z, Liang H, Zhao M, Lv Q, Liang T and Wang Y. (2022). Label‐only membership inference attacks on machine unlearning without dependence of posteriors. International Journal of Intelligent Systems. 37:11. (9424-9441). Online publication date: 26-Sep-2022.

    https://doi.org/10.1002/int.23000

  • Sadiq S, Aryani A, Demartini G, Hua W, Indulska M, Burton-Jones A, Khosravi H, Benavides-Prado D, Sellis T, Someh I, Vaithianathan R, Wang S and Zhou X. (2022). Information Resilience: the nexus of responsible and agile approaches to information use. The VLDB Journal. 10.1007/s00778-021-00720-2. 31:5. (1059-1084). Online publication date: 1-Sep-2022.

    https://link.springer.com/10.1007/s00778-021-00720-2

  • Bao E, Zhu Y, Xiao X, Yang Y, Ooi B, Tan B and Aung K. (2022). Skellam mixture mechanism. Proceedings of the VLDB Endowment. 15:11. (2348-2360). Online publication date: 1-Jul-2022.

    https://doi.org/10.14778/3551793.3551798

  • Salem A, Wen R, Backes M, Ma S and Zhang Y. (2022). Dynamic Backdoor Attacks Against Machine Learning Models 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P). 10.1109/EuroSP53844.2022.00049. 978-1-6654-1614-6. (703-718).

    https://ieeexplore.ieee.org/document/9797338/

  • Shen Y, He X, Han Y and Zhang Y. (2022). Model Stealing Attacks Against Inductive Graph Neural Networks 2022 IEEE Symposium on Security and Privacy (SP). 10.1109/SP46214.2022.9833607. 978-1-6654-1316-9. (1175-1192).

    https://ieeexplore.ieee.org/document/9833607/

  • Sun Z, Du X, Song F, Ni M and Li L. CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning. Proceedings of the ACM Web Conference 2022. (652-660).

    https://doi.org/10.1145/3485447.3512225

  • Zhao Y and Chen J. (2022). A Survey on Differential Privacy for Unstructured Data Content. ACM Computing Surveys. 54:10s. (1-28). Online publication date: 31-Jan-2022.

    https://doi.org/10.1145/3490237

  • 高 婷. (2022). Research Progress and Challenges of Membership Inference Attacks in Machine Learning. Operations Research and Fuzziology. 10.12677/ORF.2022.121001. 12:01. (1-15).

    https://www.HansPub.org/journal/doi.aspx?DOI=10.12677/ORF.2022.121001

  • Jegorova M, Kaul C, Mayor C, O'Neil A, Weir A, Murray-Smith R and Tsaftaris S. Survey: Leakage and Privacy at Inference Time. IEEE Transactions on Pattern Analysis and Machine Intelligence. 10.1109/TPAMI.2022.3229593. (1-20).

    https://ieeexplore.ieee.org/document/9987657/

  • Chen X, Dong Y, Sun Z, Zhai S, Shen Q and Wu Z. (2022). Kallima: A Clean-Label Framework for Textual Backdoor Attacks. Computer Security – ESORICS 2022. 10.1007/978-3-031-17140-6_22. (447-466).

    https://link.springer.com/10.1007/978-3-031-17140-6_22

  • Kotevska O, Alamudun F and Stanley C. (2021). Optimal Balance of Privacy and Utility with Differential Privacy Deep Learning Frameworks 2021 International Conference on Computational Science and Computational Intelligence (CSCI). 10.1109/CSCI54926.2021.00141. 978-1-6654-5841-2. (425-430).

    https://ieeexplore.ieee.org/document/9799176/

  • Rahimian S, Orekondy T and Fritz M. Differential Privacy Defenses and Sampling Attacks for Membership Inference. Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security. (193-202).

    https://doi.org/10.1145/3474369.3486876

  • Zhao B, Agrawal A, Coburn C, Asghar H, Bhaskar R, Kaafar M, Webb D and Dickinson P. (2021). On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models 2021 IEEE European Symposium on Security and Privacy (EuroS&P). 10.1109/EuroSP51992.2021.00025. 978-1-6654-1491-3. (232-251).

    https://ieeexplore.ieee.org/document/9581166/

  • Diaz M, Kairouz P, Liao J and Sankar L. Neural Network-based Estimation of the MMSE. 2021 IEEE International Symposium on Information Theory (ISIT). (1023-1028).

    https://doi.org/10.1109/ISIT45174.2021.9518063

  • Miao Y, Xue M, Chen C, Pan L, Zhang J, Zhao B, Kaafar D and Xiang Y. (2020). The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services. Proceedings on Privacy Enhancing Technologies. 10.2478/popets-2021-0012. 2021:1. (209-228). Online publication date: 1-Jan-2021.. Online publication date: 1-Jan-2021.

    https://petsymposium.org/popets/2021/popets-2021-0012.php

  • Aberkane A, Poels G and Broucke S. Exploring Automated GDPR-Compliance in Requirements Engineering: A Systematic Mapping Study. IEEE Access. 10.1109/ACCESS.2021.3076921. 9. (66542-66559).

    https://ieeexplore.ieee.org/document/9420457/

  • Tiwari K, Shukla S and George J. (2021). A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning. Data Science and Security. 10.1007/978-981-16-4486-3_3. (19-41).

    https://link.springer.com/10.1007/978-981-16-4486-3_3

  • Hisamoto S, Post M and Duh K. (2020). Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?. Transactions of the Association for Computational Linguistics. 10.1162/tacl_a_00299. 8. (49-63). Online publication date: 1-Dec-2020.

    https://direct.mit.edu/tacl/article/43536

  • Zanella-Béguelin S, Wutschitz L, Tople S, Rühle V, Paverd A, Ohrimenko O, Köpf B and Brockschmidt M. Analyzing Information Leakage of Updates to Natural Language Models. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. (363-375).

    https://doi.org/10.1145/3372297.3417880

  • Tople S, Sharma A and Nori A. Alleviating privacy attacks via causal learning. Proceedings of the 37th International Conference on Machine Learning. (9537-9547).

    /doi/10.5555/3524938.3525822

  • Thomas A, Adelani D, Davody A, Mogadala A and Klakow D. (2020). Investigating the Impact of Pre-trained Word Embeddings on Memorization in Neural Networks. Text, Speech, and Dialogue. 10.1007/978-3-030-58323-1_30. (273-281).

    http://link.springer.com/10.1007/978-3-030-58323-1_30

  • Feyisetan O, Diethe T and Drake T. (2019). Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text 2019 IEEE International Conference on Data Mining (ICDM). 10.1109/ICDM.2019.00031. 978-1-7281-4604-1. (210-219).

    https://ieeexplore.ieee.org/document/8970912/