{"list":"other","total":89,"rows":[{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1704.00028","title":"Improved Training of Wasserstein GANs","created":"2017-03-31","cited_by_count":2235},{"id":"2012.07805","title":"Extracting Training Data from Large Language Models","created":"2020-12-14","cited_by_count":2189},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"2112.03570","title":"Membership Inference Attacks From First Principles","created":"2021-12-07","cited_by_count":878},{"id":"2003.14053","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","created":"2020-03-31","cited_by_count":712},{"id":"2310.01728","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","created":"2023-10-03","cited_by_count":700},{"id":"1907.00503","title":"Modeling Tabular data using Conditional GAN","created":"2019-07-01","cited_by_count":699},{"id":"1609.02943","title":"Stealing Machine Learning Models via Prediction APIs","created":"2016-09-09","cited_by_count":619},{"id":"1610.02527","title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence","created":"2016-10-08","cited_by_count":564},{"id":"1911.00222","title":"Federated Learning with Differential Privacy: Algorithms and Performance Analysis","created":"2019-11-01","cited_by_count":536},{"id":"1806.01246","title":"ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models","created":"2018-06-04","cited_by_count":498},{"id":"1712.07557","title":"Differentially Private Federated Learning: A Client Level Perspective","created":"2017-12-20","cited_by_count":459},{"id":"2007.14390","title":"Flower: A Friendly Federated Learning Research Framework","created":"2020-07-28","cited_by_count":455},{"id":"2002.09797","title":"Reliable Fidelity and Diversity Metrics for Generative Models","created":"2020-02-23","cited_by_count":434},{"id":"2109.12298","title":"Opacus: User-Friendly Differential Privacy Library in PyTorch","created":"2021-09-25","cited_by_count":383},{"id":"2001.02610","title":"iDLG: Improved Deep Leakage from Gradients","created":"2020-01-08","cited_by_count":382},{"id":"1702.07464","title":"Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning","created":"2017-02-24","cited_by_count":334},{"id":"1706.02633","title":"Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs","created":"2017-06-08","cited_by_count":333},{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321},{"id":"2004.12088","title":"SplitFed: When Federated Learning Meets Split Learning","created":"2020-04-25","cited_by_count":310},{"id":"1407.6981","title":"RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response","created":"2014-07-25","cited_by_count":272},{"id":"1911.07963","title":"Can You Really Backdoor Federated Learning?","created":"2019-11-18","cited_by_count":255},{"id":"2104.07914","title":"Federated Learning for Internet of Things: A Comprehensive Survey","created":"2021-04-16","cited_by_count":252},{"id":"1911.07135","title":"The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks","created":"2019-11-17","cited_by_count":246},{"id":"1807.05852","title":"Machine Learning with Membership Privacy using Adversarial Regularization","created":"2018-07-16","cited_by_count":186},{"id":"1805.02628","title":"PRADA: Protecting against DNN Model Stealing Attacks","created":"2018-05-07","cited_by_count":165},{"id":"1704.03578","title":"A Survey on Homomorphic Encryption Schemes: Theory and Implementation","created":"2017-04-12","cited_by_count":156},{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153},{"id":"2007.07646","title":"A Survey of Privacy Attacks in Machine Learning","created":"2020-07-15","cited_by_count":146},{"id":"1904.01067","title":"Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning","created":"2019-04-01","cited_by_count":122},{"id":"1906.03006","title":"Reconstruction and Membership Inference Attacks against Generative Models","created":"2019-06-07","cited_by_count":122},{"id":"1910.08902","title":"Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations","created":"2019-10-20","cited_by_count":119},{"id":"2001.09700","title":"DP-CGAN: Differentially Private Synthetic Data and Label Generation","created":"2020-01-27","cited_by_count":118},{"id":"2310.10196","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook","created":"2023-10-16","cited_by_count":110},{"id":"2311.06062","title":"Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration","created":"2023-11-10","cited_by_count":98},{"id":"2006.07228","title":"FedGAN: Federated Generative Adversarial Networks for Distributed Data","created":"2020-06-12","cited_by_count":90},{"id":"1804.11238","title":"Privacy Preserving Machine Learning: Threats and Solutions","created":"2018-03-27","cited_by_count":79},{"id":"2009.03561","title":"Local and Central Differential Privacy for Robustness and Privacy in Federated Learning","created":"2020-09-08","cited_by_count":78},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66},{"id":"2004.05703","title":"DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments","created":"2020-04-12","cited_by_count":58},{"id":"1803.10266","title":"Privacy-preserving Prediction","created":"2018-03-27","cited_by_count":48},{"id":"2107.11630","title":"Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them","created":"2021-07-24","cited_by_count":48},{"id":"1807.11023","title":"A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security","created":"2018-07-29","cited_by_count":42},{"id":"2202.02691","title":"TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network","created":"2022-02-06","cited_by_count":41},{"id":"2304.01669","title":"Re-thinking Model Inversion Attacks Against Deep Neural Networks","created":"2023-04-04","cited_by_count":39},{"id":"2303.12233","title":"LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation","created":"2023-03-21","cited_by_count":36},{"id":"2201.11248","title":"Electrical Load Forecasting Using Edge Computing and Federated Learning","created":"2022-01-27","cited_by_count":34},{"id":"2103.06232","title":"Quantum machine learning with differential privacy","created":"2021-03-10","cited_by_count":31},{"id":"2209.07807","title":"Model Inversion Attacks against Graph Neural Networks","created":"2022-09-16","cited_by_count":26},{"id":"1904.06606","title":"Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks","created":"2019-04-13","cited_by_count":18},{"id":"2310.18382","title":"From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks","created":"2023-10-27","cited_by_count":16},{"id":"2503.09022","title":"Prompt Inversion Attack against Collaborative Inference of Large Language Models","created":"2025-03-12","cited_by_count":15},{"id":"2602.16800","title":"Large-scale online deanonymization with LLMs","created":"2026-02-18","cited_by_count":14},{"id":"2210.10524","title":"Over-the-Air Computation for 6G: Foundations, Technologies, and Applications","created":"2022-10-19","cited_by_count":12},{"id":"2309.16398","title":"Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey","created":"2023-09-28","cited_by_count":12},{"id":"2202.12968","title":"Does Label Differential Privacy Prevent Label Inference Attacks?","created":"2022-02-25","cited_by_count":11},{"id":"2310.10549","title":"Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey","created":"2023-10-16","cited_by_count":11},{"id":"2506.13972","title":"Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble","created":"2025-06-16","cited_by_count":11},{"id":"2101.10569","title":"Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges","created":"2021-01-26","cited_by_count":10},{"id":"2406.03820","title":"A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions","created":"2024-06-06","cited_by_count":10},{"id":"2506.10424","title":"SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks","created":"2025-06-12","cited_by_count":10},{"id":"2008.03252","title":"A Survey on Security and Privacy Issues in Edge Computing-Assisted Internet of Things","created":"2020-08-05","cited_by_count":9},{"id":"2201.07387","title":"Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home","created":"2022-01-19","cited_by_count":8},{"id":"2310.17788","title":"Utilizing Language Models for Energy Load Forecasting","created":"2023-10-26","cited_by_count":7},{"id":"2411.06773","title":"Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks","created":"2024-11-11","cited_by_count":6},{"id":"2412.06113","title":"Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions","created":"2024-12-09","cited_by_count":5},{"id":"2506.08435","title":"Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings","created":"2025-06-10","cited_by_count":5},{"id":"2205.12742","title":"SoK: Hardware-supported Trusted Execution Environments","created":"2022-05-25","cited_by_count":4},{"id":"2402.14042","title":"Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Records","created":"2024-02-21","cited_by_count":4},{"id":"2505.07239","title":"Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity","created":"2025-05-12","cited_by_count":4},{"id":"2503.10792","title":"Byzantine-Resilient Federated Learning via Distributed Optimization","created":"2025-03-13","cited_by_count":3},{"id":"2212.02042","title":"Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning","created":"2022-12-05","cited_by_count":2},{"id":"2405.17453","title":"Joint Communication and Over-the-Air Computation for Semi-Federated Learning Towards Scalable AI in Computing-Heterogeneous IoT Systems","created":"2024-05-22","cited_by_count":2},{"id":"2504.17523","title":"From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy","created":"2025-04-24","cited_by_count":2},{"id":"2510.12908","title":"Local Differential Privacy for Federated Learning with Fixed Memory Usage and Per-Client Privacy","created":"2025-10-14","cited_by_count":2},{"id":"2310.19163","title":"RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation","created":"2023-10-29","cited_by_count":1},{"id":"2403.01438","title":"Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting","created":"2024-03-03","cited_by_count":1},{"id":"2502.10599","title":"Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities","created":"2025-02-14","cited_by_count":1},{"id":"2503.21154","title":"Federated Learning with Differential Privacy: An Utility-Enhanced Approach","created":"2025-03-27","cited_by_count":1},{"id":"2511.07505","title":"FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models","created":"2025-11-10","cited_by_count":1}],"next_cursor":null}