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Dp-Sgd-Global-Adapt-V2-S: Triad Improvements of Privacy, Accuracy and Fairness Via Step Decay Noise Multiplier and Step Decay Upper Clipping Threshold

https://doi.org/10.2139/ssrn.4906113
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Microstructural porosity in additive manufacturing: The formation and detection of pores in metal parts fabricated by powder bed fusion
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Calibrating Noise to Sensitivity in Private Data Analysis
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Understanding the legal provisions that allow processing and profiling of personal data—an analysis of GDPR provisions and principles
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Deep Learning and Implementations in Banking
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3D printing: State of the art and future perspectives
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Thermal-porosity characterization data of additively manufactured Ti–6Al–4V thin-walled structure via laser engineered net shaping
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In-situ layer-wise certification for direct laser deposition processes based on thermal image series analysis
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In-situ point cloud fusion for layer-wise monitoring of additive manufacturing
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Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review
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End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
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In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes
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Effects of porosity on the mechanical properties of additively manufactured components: a critical review
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Gradient-based learning applied to document recognition
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Differential Privacy Techniques for Cyber Physical Systems: A Survey
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A Methodology for Formalizing Model-Inversion Attacks
inferredcites →https://doi.org/10.1109/csf.2017.11openalex-extracted
Rényi Differential Privacy
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On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models
inferredcites →https://doi.org/10.1109/jiot.2021.3057419openalex-extracted
Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges
inferredcites →https://doi.org/10.1109/sp.2017.41openalex-extracted
Membership Inference Attacks Against Machine Learning Models
inferredcites →https://doi.org/10.1109/tsc.2019.2897554openalex-extracted
Demystifying Membership Inference Attacks in Machine Learning as a Service
inferredcites →https://doi.org/10.1109/wacv48630.2021.00121openalex-extracted
Adaptive Privacy Preserving Deep Learning Algorithms for Medical Data
inferredcites →https://doi.org/10.1111/rssb.12454openalex-extracted
Gaussian Differential Privacy
inferredcites →https://doi.org/10.1115/1.4043898openalex-extracted
Layer-Wise Modeling and Anomaly Detection for Laser-Based Additive Manufacturing
inferredcites →https://doi.org/10.1115/1.4056488openalex-extracted
Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes
inferredcites →https://doi.org/10.1145/1536414.1536466openalex-extracted
Differential privacy and robust statistics
inferredcites →https://doi.org/10.1145/1810891.1810915openalex-extracted
Programming with differential privacy
inferredcites →https://doi.org/10.1145/2810103.2813677openalex-extracted
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
inferredcites →https://doi.org/10.1145/2976749.2978318openalex-extracted
Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.1145/3154793openalex-extracted
Attribute Inference Attacks in Online Social Networks
inferredcites →https://doi.org/10.1145/3188745.3188946openalex-extracted
Composable and versatile privacy via truncated CDP
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Neither Private Nor Fair
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Removing Disparate Impact on Model Accuracy in Differentially Private Stochastic Gradient Descent
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Membership Inference Attacks on Machine Learning: A Survey
inferredcites →https://doi.org/10.1145/773153.773173openalex-extracted
Revealing information while preserving privacy
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Deep learning in finance and banking: A literature review and classification
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Differential privacy: its technological prescriptive using big data
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The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.1609/aaai.v37i9.26242openalex-extracted
Differentially Private Learning with Per-Sample Adaptive Clipping
inferredcites →https://doi.org/10.29012/jpc.870openalex-extracted
Numerical Composition of Differential Privacy
inferredcites →https://doi.org/10.48550/arxiv.2006.15429openalex-extracted
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
inferredcites →https://doi.org/10.48550/arxiv.2102.06171openalex-extracted
High-Performance Large-Scale Image Recognition Without Normalization
inferredcites →https://doi.org/10.48550/arxiv.2106.07830openalex-extracted
On the Convergence and Calibration of Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.48550/arxiv.2109.12298openalex-extracted
Opacus: User-Friendly Differential Privacy Library in PyTorch
inferredcites →https://doi.org/10.48550/arxiv.2206.07737openalex-extracted
Disparate Impact in Differential Privacy from Gradient Misalignment
inferredcites →https://doi.org/10.48550/arxiv.2206.13033openalex-extracted
Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization
inferredcites →https://doi.org/10.52202/075280-1808openalex-extracted
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-extracted
Learning Multiple Layers of Features from Tiny Images
inferredcites →https://doi.org/10.48550/arxiv.1606.05893openalex-snapshot
You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors
inferredcites →https://doi.org/10.48550/arxiv.1807.09173openalex-snapshot
Towards Demystifying Membership Inference Attacks
inferredcites →https://doi.org/10.48550/arxiv.2009.06389openalex-snapshot
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
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