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A Methodology for Formalizing Model-Inversion Attacks
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Calibrating Noise to Sensitivity in Private Data Analysis
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Some bounds on multiparty communication complexity of pointer jumping
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Analysis of Boolean Functions
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Lower Bounds for Multi-Player Pointer Jumping
inferredcites →https://doi.org/10.1109/cvpr.2014.127openalex-snapshot
Image Reconstruction from Bag-of-Visual-Words
inferredcites →https://doi.org/10.1109/focs.2010.14openalex-snapshot
The Limits of Two-Party Differential Privacy
inferredcites →https://doi.org/10.1109/focs.2013.77openalex-snapshot
A Tight Bound for Set Disjointness in the Message-Passing Model
inferredcites →https://doi.org/10.1109/focs.2015.46openalex-snapshot
Robust Traceability from Trace Amounts
inferredcites →https://doi.org/10.1109/sfcs.2003.1238221openalex-snapshot
Gossip-based computation of aggregate information
inferredcites →https://doi.org/10.1109/sp.2012.43openalex-snapshot
Quid-Pro-Quo-tocols: Strengthening Semi-honest Protocols with Dual Execution
inferredcites →https://doi.org/10.1109/tpami.2013.228openalex-snapshot
From Bits to Images: Inversion of Local Binary Descriptors
inferredcites →https://doi.org/10.1137/1.9781611973105.102openalex-snapshot
The Power of Linear Reconstruction Attacks
inferredcites →https://doi.org/10.1145/2382559.2382562openalex-snapshot
Approximating the Influence of Monotone Boolean Functions in O(√n) Query Complexity
inferredcites →https://doi.org/10.1145/2810103.2813677openalex-snapshot
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
inferredcites →https://doi.org/10.1145/3149.214121openalex-snapshot
Impossibility of distributed consensus with one faulty process
inferredcites →https://doi.org/10.1145/773153.773173openalex-snapshot
Revealing information while preserving privacy
inferredcites →https://doi.org/10.4230/lipics.approx-random.2014.692openalex-snapshot
Space Pseudorandom Generators by Communication Complexity Lower Bounds
inferredcites →https://doi.org/10.48550/arxiv.1211.1265openalex-snapshot
From Bits to Images: Inversion of Local Binary Descriptors
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Toward Privacy-Enhancing Ambulatory-Based Well-Being Monitoring: Investigating User Re-Identification Risk in Multimodal Data
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Subject Property Inference Attack in Collaborative Learning
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A Novel Confidence Score Exploiting Attacks on Classification Trees and Random Forest Classifiers
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Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data
inferred← citeshttps://doi.org/10.1109/jbhi.2022.3204455openalex-snapshot
E2EGI: End-to-End Gradient Inversion in Federated Learning
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Breaking Hardware Boundaries of IoT Devices via Inverse Feature Completion
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Unraveling Attacks to Machine-Learning-Based IoT Systems: A Survey and the Open Libraries Behind Them
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Rethinking Certification for Trustworthy Machine-Learning-Based Applications
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Privacy Leakage Avoidance with Switching Ensembles
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FLSN-MVO: Edge Computing and Privacy Protection Based on Federated Learning Siamese Network With Multi-Verse Optimization Algorithm for Industry 5.0
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Model Inversion Attacks for Prediction Systems: Without Knowledge of Non-Sensitive Attributes
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DP-MTFL: Differentially Private Multi-Tier Federated Learning for IoT applications
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Towards Practical Differentially Private Convex Optimization
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Is Private Learning Possible with Instance Encoding?
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SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
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Training Data Leakage via Imperceptible Backdoor Attack
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An Efficient Framework for Detecting Model Inversion Attacks in Connected Healthcare Systems
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It is All in the System's Parameters: Privacy and Security Issues in Transforming Biometric Raw Data into Binary Strings
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Detecting Adversarial Image Examples in Deep Neural Networks with Adaptive Noise Reduction
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Defending Data Inference Attacks Against Machine Learning Models by Mitigating Prediction Distinguishability
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Model Inversion Attack by Integration of Deep Generative Models: Privacy-Sensitive Face Generation From a Face Recognition System
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Label-Only Model Inversion Attacks: Attack With the Least Information
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Analysis and Utilization of Hidden Information in Model Inversion Attacks
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LDS-FL: Loss Differential Strategy Based Federated Learning for Privacy Preserving
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Model Inversion Attacks Against Graph Neural Networks
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Survey: Leakage and Privacy at Inference Time
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Preserving Model Privacy for Machine Learning in Distributed Systems
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On the Role of Data Anonymization in Machine Learning Privacy
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Do Auto-Regressive Models Protect Privacy? Inferring Fine-Grained Energy Consumption From Aggregated Model Parameters
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Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection
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Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment
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Duet: an expressive higher-order language and linear type system for statically enforcing differential privacy
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Text Captcha Is Dead? A Large Scale Deployment and Empirical Study
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Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features
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Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency
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inferred← citeshttps://doi.org/10.1145/3548606.3560573openalex-snapshot
Feature Inference Attack on Shapley Values
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Group Property Inference Attacks Against Graph Neural Networks
inferred← citeshttps://doi.org/10.1145/3548606.3560663openalex-snapshot
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Privacy-preserving Deep-learning Models for Fingerprint Data using Differential Privacy
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Model Inversion Attack via Dynamic Memory Learning
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A Survey of Privacy Attacks in Machine Learning
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PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation
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Analyzing Inference Privacy Risks Through Gradients In Machine Learning
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Analysis and Measurement of Attack Resilience of Differential Privacy
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Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
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Benchmarking Relaxed Differential Privacy in Private Learning: A Comparative Survey
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Attack Vectors for Face Recognition Systems: A Comprehensive Review
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Secure Two-Party Decision Tree Classification Based on Function Secret Sharing
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Trojaning Attack on Neural Networks
inferred← citeshttps://doi.org/10.14722/ndss.2022.24335openalex-snapshot
MIRROR: Model Inversion for Deep Learning Network with High Fidelity
inferred← citeshttps://doi.org/10.15476/elte.2021.124openalex-snapshot
Federated Learning of Artificial Neural Networks
inferred← citeshttps://doi.org/10.1587/transinf.2017icp0013openalex-snapshot
Model Inversion Attacks for Online Prediction Systems: Without Knowledge of Non-Sensitive Attributes
inferred← citeshttps://doi.org/10.1609/aaai.v35i12.17284openalex-snapshot
How Does Data Augmentation Affect Privacy in Machine Learning?
inferred← citeshttps://doi.org/10.1609/aaai.v35i13.17387openalex-snapshot
Improving Robustness to Model Inversion Attacks via Mutual Information Regularization
inferred← citeshttps://doi.org/10.1609/aaai.v37i12.26731openalex-snapshot
Similarity Distribution Based Membership Inference Attack on Person Re-identification
inferred← citeshttps://doi.org/10.18653/v1/2021.emnlp-main.154openalex-snapshot
Reconstruction Attack on Instance Encoding for Language Understanding
inferred← citeshttps://doi.org/10.18653/v1/2021.privatenlp-1.1openalex-snapshot
Understanding Unintended Memorization in Language Models Under Federated Learning
inferred← citeshttps://doi.org/10.18653/v1/2022.naacl-srw.22openalex-snapshot
Differentially Private Instance Encoding against Privacy Attacks
inferred← citeshttps://doi.org/10.23919/splitech58164.2023.10193009openalex-snapshot
Improving the Robustness of DNNs-based Network Intrusion Detection Systems through Adversarial Training
inferred← citeshttps://doi.org/10.2478/popets-2019-0015openalex-snapshot
Private Evaluation of Decision Trees using Sublinear Cost
inferred← citeshttps://doi.org/10.2478/popets-2020-0052openalex-snapshot
Exposing Private User Behaviors of Collaborative Filtering via Model Inversion Techniques
inferred← citeshttps://doi.org/10.24963/ijcai.2021/327openalex-snapshot
Contrastive Model Invertion for Data-Free Knolwedge Distillation
inferred← citeshttps://doi.org/10.24963/ijcai.2021/516openalex-snapshot
GraphMI: Extracting Private Graph Data from Graph Neural Networks
inferred← citeshttps://doi.org/10.3233/jcs-191362openalex-snapshot
Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning
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Beyond Recognition : Privacy Protections in a Surveilled World
inferred← citeshttps://doi.org/10.3390/app15020647openalex-snapshot
Privacy Auditing in Differential Private Machine Learning: The Current Trends
inferred← citeshttps://doi.org/10.3390/jcp3020010openalex-snapshot
Cybersecurity for AI Systems: A Survey
inferred← citeshttps://doi.org/10.36227/techrxiv.170956580.07799070/v1openalex-snapshot
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey
inferred← citeshttps://doi.org/10.36227/techrxiv.18254723openalex-snapshot
A Survey of Image Gradient Inversion Against Federated Learning
inferred← citeshttps://doi.org/10.36227/techrxiv.18254723.v1openalex-snapshot
A Survey of Image Gradient Inversion Against Federated Learning
inferred← citeshttps://doi.org/10.3837/tiis.2021.03.015openalex-snapshot
Model Inversion Attack: Analysis under Gray-box Scenario on Deep Learning based Face Recognition System
inferred← citeshttps://doi.org/10.48550/arxiv.1902.08552openalex-snapshot
Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment
inferred← citeshttps://doi.org/10.48550/arxiv.1905.03871openalex-snapshot
Differentially Private Learning with Adaptive Clipping
inferred← citeshttps://doi.org/10.48550/arxiv.1909.02481openalex-snapshot
Duet: An Expressive Higher-order Language and Linear Type System for Statically Enforcing Differential Privacy
inferred← citeshttps://doi.org/10.48550/arxiv.1909.08362openalex-snapshot
Non-Interactive Private Decision Tree Evaluation
inferred← citeshttps://doi.org/10.48550/arxiv.1909.11835openalex-snapshot
GAMIN: An Adversarial Approach to Black-Box Model Inversion
inferred← citeshttps://doi.org/10.48550/arxiv.2004.12254openalex-snapshot
Privacy in Deep Learning: A Survey
inferred← citeshttps://doi.org/10.48550/arxiv.2005.03915openalex-snapshot
Defending Model Inversion and Membership Inference Attacks via Prediction Purification
inferred← citeshttps://doi.org/10.48550/arxiv.2006.07490openalex-snapshot
Understanding Unintended Memorization in Federated Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2007.03856openalex-snapshot
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2008.06570openalex-snapshot
Fast Dimension Independent Private AdaGrad on Publicly Estimated Subspaces
inferred← citeshttps://doi.org/10.48550/arxiv.2009.10031openalex-snapshot
Training Production Language Models without Memorizing User Data
inferred← citeshttps://doi.org/10.48550/arxiv.2012.03404openalex-snapshot
Black-box Model Inversion Attribute Inference Attacks on Classification Models
inferred← citeshttps://doi.org/10.48550/arxiv.2101.02656openalex-snapshot
Adversarial Machine Learning for 5G Communications Security
inferred← citeshttps://doi.org/10.48550/arxiv.2102.12677openalex-snapshot
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2105.08584openalex-snapshot
Contrastive Model Inversion for Data-Free Knowledge Distillation
inferred← citeshttps://doi.org/10.48550/arxiv.2108.04417openalex-snapshot
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
inferred← citeshttps://doi.org/10.48550/arxiv.2108.09033openalex-snapshot
UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning
inferred← citeshttps://doi.org/10.5441/002/edbt.2020.12openalex-snapshot
Differentially-Private Next-Location Prediction with Neural Networks
inferred← citeshttps://doi.org/10.56553/popets-2022-0079openalex-snapshot
Deletion inference, reconstruction, and compliance in machine (un)learning
inferred← citeshttps://doi.org/10.56553/popets-2023-0012openalex-snapshot
Exploring Model Inversion Attacks in the Black-box Setting
inferred← citeshttps://doi.org/10.56553/popets-2023-0092openalex-snapshot
A Method for Securely Comparing Integers using Binary Trees
inferred← citeshttps://doi.org/10.5755/j01.itc.53.4.37737openalex-snapshot
A Survey on Privacy Attacks and Defenses in Graph Neural Networks
inferred← citeshttps://doi.org/10.70675/10bb5f06zd04az4edbzadfcz7fabd892f109openalex-snapshot
Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning
inferred← citeshttps://doi.org/10.70675/4750e977z8d15z41fez8542z9899c2e11952openalex-snapshot
Leakage of Sensitive Data from Deep Neural Networks
inferred← citeshttps://doi.org/10.70675/d0edb18bzae2bz48f8zba04z48d7240e8e0dopenalex-snapshot
Collateral-Free Learning of Deep Representations : From Natural Images to Biomedical Applications
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