Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Cited by (998)

Items 401–500 of 998

1. On Inferring Training Data Attributes in Machine Learning Models
   Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Raghav Bhaskar, Mohamed Ali Kâafar · 2019 · arXiv (Cornell University)
2. Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
   Franziska Boenisch, Philip Sperl, Konstantin Böttinger · 2021 · arXiv (Cornell University)
3. Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach
   Yipeng Zhou, Runze Wang, Jiahao Liu, Di Wu, Shui Yu, Yonggang Wen · 2023 · IEEE Transactions on Dependable and Secure Computing
4. An overview of proposals towards the privacy-preserving publication of trajectory data
   Àlex Miranda-Pascual, Patricia Guerra-Balboa, Javier Parra‐Arnau, Jordi Forné, Thorsten Strufe · 2024 · International Journal of Information Security
5. Retracted: Convolutional neural network based on differential privacy in exponential attenuation mode for image classification
   Jingjing Yang, Jinzhao Wu, Xiaojing Wang · 2020 · IET Image Processing · Retracted
6. Enhanced Mixup Training: a Defense Method Against Membership Inference Attack
   Zongqi Chen, Hongwei Li, Meng Hao, Guowen Xu · 2021 · Lecture notes in computer science
7. Dissecting Distribution Inference
   Anshuman Suri, Yifu Lu, Yanjin Chen, David Evans · 2023 · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
8. Similarity Distribution Based Membership Inference Attack on Person Re-identification
   Junyao Gao, Xinyang Jiang, Huishuai Zhang, Yifan Yang, Shuguang Dou, Dongsheng Li, Duoqian Miao, Cheng Dan Deng, Cairong Zhao · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
9. Deep Neural Network Quantization Framework for Effective Defense against Membership Inference Attacks
   Azadeh Famili, Yingjie Lao · 2023 · Sensors
10. GBMIA: Gradient-based Membership Inference Attack in Federated Learning
    Xiaodong Wang, Naiyu Wang, Longfei Wu, Zhitao Guan, Xiaojiang Du, Mohsen Guizani · 2023 · International Conference on Communications
11. Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms
    Alya Alshammari, Khalil M. El Hindi · 2024 · Applied Sciences
12. Eureka: A General Framework for Black-box Differential Privacy Estimators
    Yun Lu, Malik Magdon‐Ismail, Yu Wei, Vassilis Zikas · 2024 · IEEE Symposium on Security and Privacy (S&P)
13. DeepMemory: Model-based Memorization Analysis of Deep Neural Language Models
    Derui Zhu, Jinfu Chen, Weiyi Shang, Xuebing Zhou, Jens Großklags, Ahmed E. Hassan · 2021 · IEEE/ACM International Conference on Automated Software Engineering (ASE)
14. Detecting and Classifying Self-Deleting Windows Malware Using Prefetch Files
    Adam Duby, Teryl Taylor, Gedare Bloom, Yanyan Zhuang · 2022 · IEEE Annual Computing and Communication Workshop and Conference (CCWC)
15. Assessing the Impact of Membership Inference Attacks on Classical Machine Learning Algorithms
    Gonzalo Martínez Ruiz de Arcaute, José Alberto Hernández, Pedro Reviriego · 2022 · International Conference on the Design of Reliable Communication Networks (DRCN)
16. Privacy Leakage in Text Classification A Data Extraction Approach
    Adel Elmahdy, Huseyin A. Inan, Robert Sim · 2022 · Workshop on Privacy in Natural Language Processing (PrivateNLP)
17. MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members
    Ismat Jarin, Birhanu Eshete · 2023 · Proceedings on Privacy Enhancing Technologies
18. Quantifying identifiability to choose and audit ϵ in differentially private deep learning
    Daniel Bernau, Günther Eibl, Philip W. Grassal, Hannah Keller, Florian Kerschbaum · 2021 · Proceedings of the VLDB Endowment
19. Efficient Per-Example Gradient Computations in Convolutional Neural Networks
    Gaspar Rochette, Andre Manoel, Eric W. Tramel · 2019 · arXiv (Cornell University)
20. Privacy-Preserving Multi-Party Contextual Bandits
    Awni Hannun, Brian Knott, Shubho Sengupta, Laurens J. P. van der Maaten · 2019 · arXiv (Cornell University)
21. MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
    Yixi Xu, Sumit Mukherjee, Xiyang Liu, Tople, Shruti, Dodhia, Rahul, Juan Lavista Ferres · 2020 · arXiv (Cornell University)
22. FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries
    Seng Pei Liew, Tsubasa Takahashi · 2020 · arXiv (Cornell University)
23. Membership Inference Attacks Against Semantic Segmentation Models
    Tomáš Chobola, Dmitrii Usynin, Georgios A. Kaissis · 2023 · ACM Workshop on Artificial Intelligence and Security (AISec)
24. Don't Forget Too Much: Towards Machine Unlearning on Feature Level
    Heng Yu Xu, Tianqing Zhu, Wanlei Zhou, Wei Zhao · 2024 · IEEE Transactions on Dependable and Secure Computing
25. Uncovering Gradient Inversion Risks in Practical Language Model Training
    Xinguo Feng, Zhongkui Ma, Zihan Wang, Chegne Eu Joe, Mengyao Ma, Alsharif Abuadbba, Guangdong Bai · 2024 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
26. Comparing Different Membership Inference Attacks With a Comprehensive Benchmark
    Jun Niu, Xiaoyan Zhu, Moxuan Zeng, Ge Zhang, Qingyang Zhao, Chunhui Huang, Yangming Zhang, Suyu An, Yangzhong Wang, Xinghui Yue et al. · 2025 · IEEE Transactions on Information Forensics and Security
27. Differential Privacy for Multi-Modal Federated Learning With Modality Selection
    Chang Ma, Jun Li, Yipeng Zhou, Ming Ding, Yiyang Ni, Shi Jin · 2025 · IEEE Transactions on Information Forensics and Security
28. Towards Realistic Membership Inferences: The Case of Survey Data
    Luke A. Bauer, Vincent Bindschaedler · 2020 · Annual Computer Security Applications Conference
29. Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks
    Zihang Zou, Boqing Gong, Liqiang Wang · 2022 · Lecture notes in computer science
30. PAC Privacy: Automatic Privacy Measurement and Control of Data Processing
    Hanshen Xiao, Srinivas Devadas · 2023 · Lecture notes in computer science
31. Data Isotopes for Data Provenance in DNNs
    Emily Wenger, Xiuyu Li, Ben Y. Zhao, Vitaly Shmatikov · 2023 · Proceedings on Privacy Enhancing Technologies
32. PrivMaskFL: A private masking approach for heterogeneous federated learning in IoT
    Jing Xiong, Hong Ping Zhu · 2023 · Computer Communications
33. Position: Membership Inference Attacks Cannot Prove That a Model was Trained on Your Data
    Jie Zhang, Debeshee Das, Gautam Kamath, Florian Tramèr · 2025 · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
34. Privacy Vulnerabilities in Marginals-based Synthetic Data
    Steven Golob, Sikha Pentyala, Anuar Maratkhan, Martine De Cock · 2025 · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
35. Is My Data in Your AI? Membership Inference Test (MINT) Applied to Face Biometrics
    Daniel DeAlcala, Aythami Morales, Julián Fiérrez, Gonzalo Mancera, Rubén Tolosana, Javier Ortega-García · 2025 · IEEE Access
36. A Review of Confidentiality Threats Against Embedded Neural Network Models
    Raphaël Joud, Pierre-Alain Moëllic, Remi Bernhard, Jean-Baptiste Rigaud · 2021 · IEEE World Forum on Internet of Things (WF-IoT)
37. Defense against Membership Inference Attack Applying Domain Adaptation with Addictive Noise
    Hongwei Huang · 2021 · Journal of Computer and Communications
38. How and when to stop the co-training process
    Edita Grolman, Dvir Cohen, Tatiana Frenklach, Asaf Shabtai, Rami Puzis · 2021 · Expert Systems with Applications
39. A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning
    Kapil Tiwari, Samiksha Shukla, Jossy Paul George · 2021 · Lecture notes in networks and systems
40. Membership Inference Attacks Against Self-supervised Speech Models
    Wei-Cheng Tseng, Wei-Tsung Kao, Hung-yi Lee · 2022 · Interspeech (USB)
41. A White-Box Generator Membership Inference Attack Against Generative Models
    Maryam Azadmanesh, Behrouz Shahgholi Ghahfarokhi, Maede Ashouri Talouki · 2021 · International ISC Conference on Information Security and Cryptology (ISCISC)
42. How Differential Privacy Reinforces Privacy of Machine Learning Models?
    Sana Ben Hamida, Hichem Mrabet, Abderrazak Jemai · 2022 · Communications in computer and information science
43. On the Privacy Risks of Cell-Based NAS Architectures
    Hai Huang, Zhikun Zhang, Yun Cheng Shen, Michael Backes A, Qi Lecky Li, Yang Zhang · 2022 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
44. Towards Model Quantization on the Resilience Against Membership Inference Attacks
    Charles Kowalski, Azadeh Famili, Yingjie Lao · 2022 · Proceedings - International Conference on Image Processing
45. Designing a Location Trace Anonymization Contest
    Takao Murakami, Hiromi Arai, Koki Hamada, Takuma Hatano, Makoto Iguchi, Hiroaki Kikuchi, Atsushi Kuromasa, Hiroshi Nakagawa, Yuichi Nakamura, Kenshiro Nishiyama et al. · 2023 · Proceedings on Privacy Enhancing Technologies
46. Variations and Extensions of Information Leakage Metrics with Applications to Privacy Problems with Imperfect Statistical Information
    Shahnewaz Karim Sakib, George T. Amariucai, Yong Guan · 2023 · IEEE Computer Security Foundations Workshop (CSFW)
47. 10 Security and Privacy Problems in Large Foundation Models
    Jinyuan Jia, Hongbin Liu, Neil Zhenqiang Gong · 2023 · Springer eBooks
48. Membership Inference Attack Using Self Influence Functions
    Gilad Cohen, Raja Giryes · 2024 · IEEE Winter Conference on Applications of Computer Vision
49. CVNNs-IDS: Complex-Valued Neural Network Based In-Vehicle Intrusion Detection System
    Mu Han, Pengzhou Cheng, Shidian Ma · 2020 · Communications in computer and information science
50. When Machine Learning Models Leak: An Exploration of Synthetic Training Data
    Manel Slokom, Peter‐Paul de Wolf, Martha Larson · 2022 · Lecture notes in computer science
51. NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks
    Nuo Xu, Binghui Wang, Ran Ran, Wujie Wen, Parv Venkitasubramaniam · 2022 · Annual Computer Security Applications Conference
52. Towards Characterizing and Limiting Information Exposure in DNN Layers
    Mo, Fan, Ali Shahin Shamsabadi, Kleomenis Katevas, Andrea Cavallaro, Hamed Haddadi · 2019 · arXiv (Cornell University)
53. Exploring Private Federated Learning with Laplacian Smoothing.
    Zhicong Liang, Bao Wang, Quanquan Gu, Stanley Osher, Yuan Yao · 2020 · arXiv (Cornell University)
54. DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks
    Hongwei Huang, Weiqi Luo, Guo‐Qiang Zeng, Jian Weng, Yue Zhang, Anjia Yang · 2020 · arXiv (Cornell University)
55. The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services
    Yuantian Miao, Minhui Xue, Chao Chen, Lei Pan, Jun Zhang, Benjamin Zi Hao Zhao, Dali Kaafar, Yang Xiang · 2020 · Proceedings on Privacy Enhancing Technologies
56. Link Membership Inference Attacks against Unsupervised Graph Representation Learning
    Xiuling Wang, Hui Wang · 2023 · Annual Computer Security Applications Conference
57. A General Strategy Graph Collaborative Filtering for Recommendation Unlearning
    Yongjing Hao, Fuzhen Zhuang, Deqing Wang, Guanfeng Liu, Victor S. Sheng, Pengpeng Zhao · 2024 · ACM International Conference on Information and Knowledge Management (CIKM)
58. Defending Data Inference Attacks Against Machine Learning Models by Mitigating Prediction Distinguishability
    Ziqi Yang, Yiran Zhu, Jie Wan, ChuXiao Xiang, Tong Tang, Yilin Wang, Ruifeng Xu, Lijin Wang, Fan Zhang, Jiarong Xu et al. · 2024 · IEEE Transactions on Dependable and Secure Computing
59. Bioimpedance-based prediction of dry matter content and potato varieties through supervised machine learning methods
    Ciro Allarà, Roberto Moscetti, Giacomo Bedini, Manuela Ciocca, Alessandro Benelli, Paolo Lugli, Luisa Petti, Pietro Ibba · 2025 · Postharvest Biology and Technology
60. Burst-Sensitive Traffic Forecast via Multi-Property Personalized Fusion in Federated Learning
    Jingjing Xue, Sheng Sun, Min Liu, Yuwei Wang, Xuying Meng, Jingyuan Wang, Junbo Zhang, Ke Xu · 2025 · IEEE Transactions on Mobile Computing
61. When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
    Shang Wang, Tianqing Zhu, Dayong Ye, Wanlei Zhou · 2025 · IEEE Transactions on Dependable and Secure Computing
62. Taking Away Both Model and Data: Remember Training Data by Parameter Combinations
    Wenjian Luo, Licai Zhang, Peiyi Han, Chuanyi Liu, Rongfei Zhuang · 2022 · IEEE Transactions on Emerging Topics in Computational Intelligence
63. Detecting Unintended Memorization in Language-Model-Fused ASR
    Wei‐Chin Huang, Steve Chien, Om Dipakbhai Thakkar, Rajiv Mathews · 2022 · Interspeech (USB)
64. MTL-Leak: Privacy Risk Assessment in Multi-Task Learning
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65. Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
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66. Data Reconstruction Attack Against Principal Component Analysis
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67. Multi-Event Location Denoising Scheme for φ-OTDR Based on FFDNet Network
    Xiyu Yang, Shuai Li, Yanping Xu, Zhaojun Liu, Zengguang Qin · 2023 · Photonics
68. Comprehension from Chaos: Towards Informed Consent for Private Computation
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69. Differentially Private Federated Learning: Privacy and Utility Analysis of Output Perturbation and DP-SGD
    Anastasia Pustozerova, Jan Baumbach, Rudolf L. Mayer · 2023 · IEEE International Conference on Big Data (BigData)
70. MaskArmor: Confidence masking-based defense mechanism for GNN against MIA
    Chenyang Chen, Xiaoyu Zhang, Hongyi Qiu, Jian Lou, Zhengyang Liu, Xiaofeng Chen · 2024 · Information Sciences
71. Defending against membership inference attacks: RM Learning is all you need
    Zheng Zhang, Jianfeng Ma, Xindi Ma, Ruikang Yang, Xiangyu Wang, Junying Zhang · 2024 · Information Sciences
72. Efficient and Privacy-Preserving Integrity Verification for Federated Learning with TEEs
    Jiarui Li, Nan Chen, Shucheng Yu, Thitima Srivatanakul · 2024 · IEEE Military Communications Conference (MILCOM)
73. GLiRA: Closed-Box Membership Inference Attack via Knowledge Distillation
    Andrey V. Galichin, Mikhail Pautov, Alexey Zhavoronkin, Oleg Y. Rogov, Ivan Valer'evich Oseledets · 2025 · IEEE Transactions on Information Forensics and Security
74. Defending against attacks in deep learning with differential privacy: a survey
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75. gMINT: Gradiant-based Membership Inference Test Applied to Image Models
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76. Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features
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77. Membership Inference Attack Against Principal Component Analysis
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78. SoK: Membership Inference is Harder Than Previously Thought
    Antreas Dionysiou, Elias Athanasopoulos · 2023 · Proceedings on Privacy Enhancing Technologies
79. VPP: Privacy Preserving Machine Learning via Undervolting
    Md. Shohidul Islam, Behnam Omidi, Ihsen Alouani, Khaled N. Khasawneh · 2023 · IEEE International Symposium on Hardware Oriented Security and Trust (HOST)
80. SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning
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81. Translating theory into practice: assessing the privacy implications of concept-based explanations for biomedical AI
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82. Robots in Smart Cities
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83. Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models
    Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos · 2019 · ORCA Online Research @Cardiff (Cardiff University)
84. Evaluating differentially private decision tree model over model inversion attack
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85. CRJ 700 Aerodynamic Coefficients Identification in Dynamic Stall Conditions using Neural Networks
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86. Combing for Credentials: Active Pattern Extraction from Smart Reply
    Bargav Jayaraman, Esha Ghosh, Melissa Chase, Sambuddha Roy, Wei Dai, David M Evans · 2024 · IEEE Symposium on Security and Privacy (S&P)
87. Adversarial Privacy Preservation under Attribute Inference Attack
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88. Utility/Privacy Trade-off through the lens of Optimal Transport
    Étienne Boursier, Vianney Perchet · 2019 · arXiv (Cornell University)
89. Provable trade-offs between private & robust machine learning.
    Jamie Hayes · 2020 · arXiv (Cornell University)
90. Towards Causal Federated Learning For Enhanced Robustness and Privacy
    Sreya Francis, Irene Tenison, Irina Rish · 2021 · arXiv (Cornell University)
91. Quantifying Information Leakage from Gradients.
    Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi, Soteris Demetriou · 2021 · arXiv (Cornell University)
92. Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference
    Jiaxiang Liu, Simon Oya, Florian Kerschbaum · 2021 · arXiv (Cornell University)
93. Selective Ensembles for Consistent Predictions
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94. A Survey of Image Gradient Inversion Against Federated Learning
    Zhaohua Li, Le Wang, Guangyao Chen, Muhammad Shafq, Zhaoquan Gu · 2022
95. Proceedings of the Third Workshop on Privacy in Natural Language Processing
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96. Amnesiac Machine Learning
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97. Human Immune System Inspired Security for Federated Learning-Empowered Internet of Things
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98. An interpretable privacy-preserving real-time carbon emission estimation approach for heterogeneous industrial enterprises
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99. Dynamic differential privacy technique for deep learning models
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100. Privacy-Preserved Federated Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans
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