The paper evaluates methods for explaining deep learning in security.
problem Understanding the predictions of deep learning models in security applications.
method Developed criteria to compare and evaluate six explanation methods.
result Significant differences exist between the methods, leading to recommendations.
New algorithms secure IoT edge computing from jamming attacks.
problem Secure mobile edge computing in IoT under jamming attacks.
method Online learning tools for developing SAVE-S and SAVE-A algorithms.
result Achieves sublinear regret without extra resources.
Optimizes crypto-oriented neural architectures for faster secure inference.
problem Privacy conflicts between model users and providers in neural network applications.
method Proposes a novel Partial Activation layer to optimize the initial design of crypto-oriented neural architectures.
result Significant improvement in the efficiency of secure inference on common evaluation metrics.
Secure linear regression at speed of plaintext methods.
problem Secure multiparty linear regression and feature selection.
method Distributed algorithms combining geometric ideas.
result Efficient and secure genome-wide association studies.
Securely share encrypted data for machine learning training.
problem Securely sharing encrypted data for machine learning training without revealing the data.
method Rotation based method using flow model.
result Theoretical justification and demonstration of effectiveness in different scenarios.
Secure neural network inference on untrusted platforms using holographic reduced representations.
problem Secure neural network inference on untrusted platforms.
method Connectionist Symbolic Pseudo Secrets using Holographic Reduced Representations (HRR).
result Empirical robustness to attack under various threat models.
Blockchain as a Service offers a secure, decentralized computing solution.
problem Lack of transparency, security, and privacy in cloud computing.
method Decentralized cooperative computing process using blockchain, homomorphic encryption, and SDN.
result Performance evaluated via different scenarios in simulations.
This paper explores security threats in ML systems and proposes mitigation techniques.
problem Security vulnerabilities in ML-based systems during training and inference.
method Overview of security threats, demonstrations using LeNet and VGGNet, proposed attack.
result Demonstrated security threats and proposed mitigation techniques.
We develop a secure aggregation protocol for federated learning that reduces communication and computation costs.
problem Expensive communication and privacy concerns in federated learning.
method Adapting compression-based federated techniques to additive secret sharing.
result Our protocol achieves high accuracy with low communication costs and is more efficient than prior work.
Secure Multiparty Computation protects data privacy in Symbolic Regression.
problem Data privacy in Symbolic Regression models.
method Secure Multiparty Computation for vertical partitioning.
result Comparable performance to centralized model while preserving privacy.
SLIP secures LLMs on edge devices by splitting computation and protecting sensitive parts.
problem Protecting LLMs on edge devices from theft and unauthorized use.
method SLIP uses matrix decomposition to split model computation between secure and vulnerable resources, ensuring zero accuracy degradation and minimal latency.
result SLIP is the first practical, secure hybrid protocol for protecting LLMs on edge devices.
FastSecAgg improves federated learning security and efficiency.
problem Privacy leakage in federated learning due to model parameter sharing.
method Introduces FastSecAgg, a secure aggregation protocol with FFT-based multi-secret sharing (FastShare).
result Efficient in computation and communication, robust to client dropouts.
Secure XGB for privacy-preserving machine learning in federated learning.
problem Privacy-preserving machine learning in federated learning with practical gradient tree boosting models.
method Secure multi-party computation, distributed model storage, secure permutation protocols.
result Our XGB models provide competitive accuracy and practical performance.
Securely trains neural networks remotely with deep learning's flaws.
problem Secure and efficient training of neural networks over unsecured channels.
method Leverages deep learning's weaknesses for secure training.
result Efficient and secure training of neural networks remotely.
QFNN-FFD uses quantum computing and FL for secure financial fraud detection.
problem Financial fraud detection in the financial sector.
method Quantum Federated Neural Network (QFNN-FFD) combining QML and FL.
result Achieves precision rates above 95% and robustness against noise.
CheckNet verifies neural network inference on untrusted devices.
problem Ensuring secure and tamper-proof inference on untrusted devices.
method A checksum-based approach for neural network inference verification.
result Excellent attack detection and success bounds on various models.
Securely trains regression models with secret sharing for data collaboration.
problem Balancing data collaboration for technological improvements with security concerns.
method Secret sharing scheme for scalable and efficient secure multiparty training.
result Scalable and efficient protocols for training linear and logistic regression models.
Framework for privacy-preserving deep learning using secure data processing.
problem Ensuring privacy in deep learning models while maintaining functionality.
method Introduces a generic framework that prioritizes data ownership and secure processing, using chains of commands and tensors.
result Early results show no impact on prediction accuracy but introduce a significant performance overhead.
Paper proposes a new method to compute cryptocurrency prices securely.
problem Accurate price feeds without a third party.
method Algorithmic method to compute prices from potentially dishonest sources.
result The proposed method can report accurate prices even from dishonest sources.
Post-Quantum Secure Federated DeFi for Inclusive Banking
problem Financial systems and DeFi ecosystems are vulnerable to quantum computing threats.
method Post-Quantum Secure Federated DeFi framework using lattice-based FHE.
result End-to-end homomorphic computation enables inter-bank collaboration.
The study calculates securities lending haircuts and indemnification costs.
problem Managing borrower default risk in securities markets.
method Repo haircut model applied to securities lending transactions; quantifies haircuts and indemnification costs.
result Computed borrower-dependent haircuts and indemnification costs for US Treasuries and equities.
Securely aggregates user-held gradients in federated learning without revealing individual data.
problem Securely aggregating gradients from users in federated learning without exposing individual data.
method Novel communication-efficient Secure Aggregation protocol for high-dimensional data, tolerating up to 1/3 users failing.
result Offers significant communication efficiency for federated learning with high-dimensional data.
Secure federated learning reduces privacy risks with differential privacy and secure multiparty computation.
problem Reverse engineering of private client data from federated learning model parameters.
method Combining differential privacy and secure multiparty computation.
result Improved accuracy of shared models without significant privacy loss.
Efficiently speeds up privacy-preserving logistic regression.
problem Privacy-preserving machine learning in collaborative studies.
method Customized numerical optimization for secure computing.
result Significant acceleration of logistic regression up to 8.1x.
PrivacyFL simulates privacy-preserving federated learning.
problem Ensuring privacy in federated learning environments.
method Extensible, configurable simulator for federated learning.
result PrivacyFL checks feasibility and improves model accuracy.
Paper proposes scalable privacy-preserving DNN for industrial applications.
problem Data isolation and scalability issues in deep neural networks.
method Split computation graph into private and neutral server parts; use cryptographic techniques for private data.
result Demonstrates practicality of the proposed scalable privacy-preserving DNN.
Paper proposes efficient privacy-preserving matrix encryption for secure collaborative learning against malicious adversaries.
problem Secure collaborative learning of sensitive data across different agencies is challenging with malicious adversaries.
method The paper applies matrix encryption to secure data against chosen plaintext attack, known plaintext attack, and collusion attack, achieving local differential privacy and high computation efficiency.
result The proposed schemes are computationally efficient and secure against malicious adversaries compared to existing techniques.
Privacy concern has been increasingly important in many machine learning (ML) problems. We study empirical risk minimization (ERM) problems under secure multi-party computation (MPC) frameworks. Main technical tools for MPC have been developed based on cryptography. One of limitations in current cryptographically priva…
HASP enhances mobile security against ASR by adding imperceptible noise.
problem Security leakage from ASR on mobile devices.
method Adversarial noise to perturb WER and enhance security.
result HASP reduces WER by 84.55% and speeds up data processing.
SANNS secures k-NNS queries and results without revealing them.
problem Securely searching for nearest neighbors without exposing sensitive data.
method Optimized linear scan and clustering-based algorithm with cryptographic primitives.
result SANNS achieves up to 31x faster response times and scales to 10 million entries.
secml is a Python library for secure and explainable machine learning.
problem Evaluate and understand security and explainability of machine learning models.
method Implement attacks and explainability methods for various algorithms.
result Demonstrates how quickly models fail under adversarial perturbations and visualizes influential features.
COPML framework securely trains models across multiple data owners without revealing individual data.
problem Privacy-preserving collaborative machine learning with multiple data owners.
method Securely encodes data, distributes computation, performs distributed training.
result Achieves up to 16x speedup in training time while maintaining strong privacy.
This review covers quantum computing applications in finance and blockchain.
problem Challenges in finance and blockchain security with quantum computing.
method Systematic review of recent quantum finance and blockchain work.
result Quantum-resistant blockchain systems and security measures.
Survey on securing ML for healthcare, addressing privacy and robustness issues.
problem Security and robustness challenges in healthcare ML/DL applications.
method Overview of security and privacy methods for ML in healthcare.
result Discussion of current research challenges and future directions.
A new neural network method for efficient power system security analysis.
problem Efficiently compute load-flows for power system security analysis.
method Guided dropout technique to train a deep feed-forward neural network on n-1 problems.
result Generalization to n-2 problems without retraining, leveraging the combinatorial nature of the problem.
New framework tackles adversarial classification problems.
problem Security classification problems with adversaries.
method Adversarial risk analysis approach.
result Illustrated with examples and discussed computational issues.
Study forecasts food security trends using real-time data.
problem Food insecurity prediction for sub-national regions.
method Quantitative methodology combining various machine learning models.
result Reservoir Computing model performs best in food security prediction.
Research analyzes ethical concerns around MEV on blockchain and social media.
problem Fairness issues in transaction ordering on blockchain.
method Applied NLP methods to analyze topics in tweets on MEV.
result Tweets discussed ethical concerns like security, equity, and solutions to MEV.
Secure methods learn fair models without revealing sensitive attributes.
problem Training fair machine learning models without exposing sensitive data.
method Secure multi-party computation to encrypt sensitive attributes.
result Outcome-based fair models can be learned, checked, or verified without revealing sensitive attributes.
Quantum computing offers new solutions for financial optimization, pricing, risk, and security.
problem Core financial bottlenecks in combinatorial search, expectation estimation, and rare-event analysis.
method Identify bottlenecks, specify quantum primitives, compare with classical benchmarks, assess under constraints.
result Strongest near-term case for quantum finance in hybrid workflows, constrained search, and amplitude-estimation.
Secure federated learning framework resists adversarial users.
problem Resilience against adversarial (Byzantine) users in federated learning.
method Integrated stochastic quantization, verifiable outlier detection, and secure model aggregation.
result First single-server Byzantine-resilient secure aggregation framework (BREA) for secure federated learning.
Research tests machine learning models for cloud security.
problem Lack of real-time, unbiased datasets for machine learning in cloud security.
method Used UNSW and ISOT datasets to train and test supervised machine learning models.
result Machine learning models trained on diverse datasets show robustness in cloud security.
Bitcoin and other cryptocurrencies are vulnerable to quantum attacks, but some alternatives offer better security.
problem Quantum computers pose a threat to the security of cryptocurrencies like Bitcoin.
method Analysis of Bitcoin's proof-of-work and elliptic curve signature scheme, evaluation of alternative proof-of-work and post-quantum signature schemes.
result Bitcoin's proof-of-work is resistant to quantum speedup for the next 10 years, but elliptic curve signatures are vulnerable.
This paper applies secure multi-party computation to K-means clustering to protect private data.
problem Privacy-preserving K-means clustering for distributed private data.
method Secure multi-party computation (MPC) techniques to protect private data during K-means clustering.
result Privacy-preserving K-means clustering is feasible and effective for both horizontal and vertical data distribution.
Model for open, decentralized network with task load balancing.
problem Complex computational tasks in open, decentralized networks.
method Incentive-based load balancing using economic mechanisms.
result Optimized resource allocation and enhanced system resilience.
The paper analyzes corporate security prices in incomplete credit risk models.
problem Computing the price dynamics of traded securities in models with unobservable firm asset values.
method Transformed the stochastic filtering problem for the asset value into a filtering problem for a stopped diffusion process and applied filtering literature results.
result Obtained an SPDE-characterization for the filter density and determined the price dynamics of traded securities.
Paper accelerates and secures distributed NMF.
problem Efficiently processing large NMF matrices and maintaining data privacy.
method Proposes DSANLS framework with matrix sketching for acceleration and secure adaptation.
result DSANLS framework and secure adaptations for distributed NMF.
Article evaluates AI security threats and proposes multiple measures.
problem Threats to AI integrity and security.
method Literature review, analysis of AI supply chain, discussion of mitigations.
result Multiple protective measures are necessary for AI security.