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.
Privacy-preserving multi-party contextual bandits learn without sharing data.
problem Privacy-preserving learning for contextual bandits with multiple parties.
method Secure multi-party computation combined with epsilon-greedy differential privacy.
result Developed a privacy-preserving multi-party contextual bandit algorithm.
Biscotti uses blockchain for secure peer-to-peer ML without central coordination.
problem Secure and private multi-party machine learning with trust and poisoning attacks.
method Decentralized peer-to-peer ML using blockchain and cryptography.
result Biscotti protects privacy and model performance against 30% adversaries.
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.
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.
Secure transfer learning framework improves model flexibility without compromising privacy.
problem Scattered data across organizations limits machine learning.
method Federated Transfer Learning (FTL) framework with secure transfer cross validation.
result Models can be built more flexibly and accurately with shared labels from different sources.
Multi-party machine learning leaks global dataset properties even with black-box access.
problem Leakage of global dataset properties in multi-party machine learning.
method Demonstrated leakage of sensitive attribute distributions in pooled data.
result A curious party can infer sensitive attribute distributions in other parties' data with high accuracy.
SecureGBM securely trains GBM models across two parties without revealing data.
problem Securely training GBM models across parties with encrypted data.
method Extending LightGBM with semi-homomorphic encryption and stochastic approximation.
result SecureGBM achieves AUC within 3% of non-secure LightGBM, maintaining performance.
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.
Efficiently preserves privacy in logistic regression for IoT data.
problem Balancing data privacy and utility in collaborative learning.
method Matrix encryption approach for secure multi-party computation.
result Proposes a privacy-preserving logistic regression model with fast convergence.
FLFE improves machine learning by efficiently and securely transforming features.
problem Efficiently and securely transforming features in a multi-party setting.
method FLFE uses a pre-learning pattern to selectively transform features, reducing communication overhead.
result FLFE outperforms evaluation-based approaches in feature transformation efficiency.
HDP-VFL hybridizes DP for VFL, reducing privacy costs.
problem Privacy-preserving collaborative learning from vertically partitioned data.
method Hybrid DP framework combining HE and MPC for VFL.
result Achieves DP and JDP with negligible training time and accuracy trade-offs.
This paper optimizes SMPC for neural network inference, reducing memory and time.
problem Memory and time constraints in secure neural network inference.
method Implemented ABY2.0 protocol, optimized memory usage, and used a helper node.
result MNIST inference reduced from 8.03 GB RAM and 200s to 0.2 GB RAM and 32s.
CodedPrivateML secures ML training data and models.
problem Training machine learning models while keeping data private.
method Data and model-theoretically private approach with parallelization.
result Significant speedup over cryptographic methods.
Securely evaluates the benefits of merging datasets for causal estimation.
problem Challenges in assessing the value of merging datasets for causal treatment effect estimation.
method Cryptographically secure multi-party computation to evaluate Expected Information Gain (EIG) while ensuring privacy.
result Demonstrates the first privacy-preserving method for dataset acquisition tailored to causal estimation.
This paper analyzes privacy-preserving methods for collaborative forecasting.
problem Data owners' reluctance to share data due to competitive and privacy concerns.
method Examines three groups of privacy-preserving methods: data transformation, secure multi-party computations, and decomposition methods.
result State-of-the-art techniques have limitations in preserving data privacy, such as trade-offs between privacy and forecasting accuracy.
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…
This paper examines anomalies and frauds in blockchain networks and proposes detection techniques.
problem Anomalies and frauds undermine blockchain networks' integrity and security.
method Statistical and machine learning methods, game-theoretic solutions, digital forensics, reputation-based systems, and risk assessment techniques.
result Practical applications and insights for enhancing blockchain network security.
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.
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact,…
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entir…
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.
Protocol minimizes disclosure in classification tasks.
problem Ensuring minimal disclosure in classification protocols.
method Developed a protocol for multi-party classification that minimizes non-responsive document disclosure.
result Guarantees minimal disclosure of non-responsive documents.
Study tackles RLHF with diverse human feedback, showing limitations and proposing a meta-learning approach.
problem Traditional RLHF fails to balance diverse human preferences.
method Integrates meta-learning and multiple social welfare functions to optimize diverse preferences.
result Establishes sample complexity bounds for optimizing diverse social welfare functions.
Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that …
PBM mechanism improves privacy and accuracy in federated learning.
problem Secure and private federated learning with limited privacy budget.
method Poisson Binomial mechanism for discrete differential privacy.
result Achieves same privacy-accuracy trade-offs as Gaussian mechanism.
Private method measures nonlinear correlations between data hosted across two entities.
problem Measuring nonlinear correlations between sensitive data hosted across multiple parties while preserving privacy.
method Differentially private estimator of distance correlation.
result First private estimator of nonlinear correlations in a multi-party setup.
In this work, we demonstrate universal multi-party poisoning attacks that adapt and apply to any multi-party learning process with arbitrary interaction pattern between the parties. More generally, we introduce and study (k,p)-poisoning attacks in which an adversary controls k∈[m] of the parties, and for each cor…
A framework for partially encrypted machine learning using functional encryption.
problem Performing machine learning on encrypted data without revealing sensitive information.
method Combining adversarial training and functional encryption to efficiently compute quadratic functions and prevent feature leakage.
result The proposed framework maintains high model accuracy while significantly improving data privacy.
Masked LARk prevents cross-site tracking while training models.
problem Cross-site tracking of user data through third-party cookies.
method Secure multi-party compute (MPC) protocol with masking.
result Prevents cross-site tracking and maintains model training flexibility.
DAG-LSTM improves DA classification in group chats.
problem DA classification in multi-party conversations.
method Directed-Acyclic-Graph LSTM (DAG-LSTM) exploiting turn-taking structure.
result DAG-LSTM outperforms existing methods by 0.8% in accuracy and 1.2% in macro-F1 score.
SOTERIA optimizes neural networks for secure inference with minimal overhead.
problem Protecting user privacy in ML-as-a-service models with low overhead.
method Neural architecture search with dual objectives of accuracy and cryptographic efficiency.
result SOTERIA constructs efficient models for secure inference.
This work proposes a scalable framework for trusted multi-party computations using blockchain.
problem Ensuring trust in results from multi-agent computational experiments.
method Combining distributed validation and blockchain for immutable audits, reducing storage and communication costs.
result Guaranteed verifiability and validity of local computations in a scalable multi-agent environment.
Asynchronous federated learning for vertically partitioned data improves efficiency and privacy.
problem Efficiently train models on vertically partitioned data without a trusted third party.
method Proposed AFSGD-VP and its SVRG and SAGA variants for asynchronous federated learning.
result AFSGD-VP and its variants achieve higher efficiency than synchronous algorithms.
Industry lacks tools to secure ML systems, study finds.
problem Insufficient security tools for ML systems in industry.
method Interviews with 28 organizations to identify gaps.
result Need revised Security Development Lifecycle for ML.
Abstract: Cyber-security challenges tackled with machine learning.
problem Cyber-security problems and lack of labeled data.
method Provided novel data sets and a method to generate labels via pivoting.
result Solutions for generating labels in cyber-security.
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.
RL models improve target control in SSGs for security applications.
problem Improving RL algorithms for target control in SSGs.
method Investigates improvements to target representations in RL algorithms.
result Enhanced RL models control targets better in SSGs.
Paper proposes a secure protocol for federated learning.
problem Combining robustness, privacy, and security in federated learning.
method Secure two-server protocol for federated learning.
result Offers both input privacy and Byzantine-robustness.
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.
Explores security challenges of machine learning in real-world systems.
problem Vulnerabilities in machine learning models deployed in safety-critical systems.
method Broadens systems security view of ML vulnerabilities, identifies novel challenges, proposes mitigation suggestions.
result Highlights novel challenges and proposes mitigation strategies for securing ML systems.
This paper discusses adversarial attacks on cyber security systems using machine learning.
problem Adversarial attacks limit the use of machine learning in cyber security.
method Characterizes adversarial attack methods and their applications in cyber security.
result Highlights unique challenges and future research directions for adversarial attacks in cyber security.
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.
New method defends machine learning models from black-box attacks.
problem Protecting machine learning models from black-box transfer attacks.
method Randomized ensemble technique with provable security guarantee.
result Empirical evidence of security for random binary classifiers.
This paper reviews ML and DL for IoT security, highlighting gaps and future directions.
problem Security and privacy issues in IoT networks due to resource constraints and dynamic behavior.
method Systematic review of current security solutions and ML/ DL approaches.
result ML and DL are essential for IoT security due to resource constraints and dynamic behavior.
This paper improves federated learning efficiency by auto-tuning secure aggregation parameters.
problem Efficient communication in federated learning with quantized updates.
method Auto-tuning secure aggregation parameters based on random rotation properties.
result Improved communication efficiency in federated learning with secure aggregation.
Secure and efficient distributed learning on devices with limited communication.
problem Limited communication and security in distributed on-device learning.
method Proposes SLSGD, a robust distributed optimization algorithm with efficient communication and attack tolerance.
result Stabilizes convergence and tolerates data poisoning on a small number of workers.
Proposes a secure communication method independent of eavesdropper's decoder.
problem Lack of practical security constraints in existing methods.
method Dual MINE-based neural secure communications model.
result Security performance is not affected by eavesdropper's decoding means.