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.
SharedMF uses secret sharing to protect privacy in distributed recommendation systems.
problem Privacy issues in multi-source data for recommendation systems.
method Federated learning and secret sharing technology.
result SharedMF achieves faster execution speed and better data adaptability compared to homomorphic encryption methods.
AriaNN enables private deep learning with minimal interaction and reduced key sizes.
problem Private deep learning with minimal interaction and reduced key sizes.
method Semi-honest 2-party computation protocol with function secret sharing, optimized primitives for neural network operations.
result Efficient private comparison for ReLU operations with reduced key size and improved performance.
Matryoshka hides secret models in a carrier model, achieving high capacity and robustness.
problem Stealing functionality of private ML data by hiding models in a carrier model.
method Parameter sharing approach exploiting the learning capacity of the carrier model.
result Hides a 26x larger secret model or 8 secret models in the carrier model.
A new federated learning method protects privacy in mobile crowdsensing.
problem Data and model privacy protection in federated extreme gradient boosting for mobile crowdsensing.
method Secret sharing based federated learning architecture FedXGB.
result FedXGB achieves less than 1% accuracy loss while preserving model privacy.
A novel approach to federated learning with strong privacy guarantees.
problem Maintaining privacy of clients' data and federator's objective in federated learning.
method Inspired by knowledge distillation and private information retrieval, the approach combines secret-sharing-based multi-party computation and graph-based private information retrieval.
result Strong information-theoretic privacy guarantees for federated learning.
The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense strategy. We introduce a multi-channel architecture in a gray-box scenario, which assumes that the architecture of the classifier and the t…
Local regularization fails in transductive learning for some multiclass problems.
problem Whether local regularization can learn all transductive multiclass problems.
method Provided a negative answer by exhibiting a specific multiclass problem.
result Local regularization cannot learn all transductive multiclass problems.
This paper improves privacy accounting in decentralized FL using f-Differential Privacy.
problem Challenges in accurately quantifying privacy budget in decentralized FL.
method Develops two new f-DP-based accounting methods for decentralized FL.
result Yields tighter (ε,δ) bounds and improved utility compared to existing methods.
Secret neural networks hidden within trained models.
problem Excess capacity in neural networks allows embedding secret models.
method Novel framework for hiding secret neural networks within carrier networks.
result Detection of hidden networks is computationally infeasible.
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.
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.
This work addresses privacy issues in IoT data sharing by balancing information disclosure and user privacy.
problem Balancing privacy and utility in time-series data sharing from IoT devices.
method Formulated as POMDPs, solved using A2C DRL, evaluated with synthetic and real data.
result Proposed policies achieve a good balance between privacy and utility.
Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many commercial platforms (e.g. Amazon) and they are mainly built based on user-item interactions. Besides, social platforms (e.g. Facebook) have ri…
Proposes a method for private aggregation in heterogeneous federated learning.
problem Ensuring resilience to Byzantine clients and maintaining client data privacy in federated learning with heterogeneous data.
method Careful co-design of verifiable secret sharing, secure aggregation, and private information retrieval scheme.
result Achieves information-theoretic privacy guarantees and Byzantine resilience under data heterogeneity.
Deep-Lock secures DNN models with secret keys.
problem Preventing unauthorized usage of DNN models.
method Uses S-Boxes and key scheduling to encrypt DNN parameters.
result Ensures correct functioning only with the correct secret key.
Researchers show how to secretly train models with hidden data, detect usage with high confidence.
problem Protecting training data from traceability in large language models.
method Gradient-based optimization to learn secret sequences absent from training data.
result Secret sequences can be learned by models without performance degradation, detectable with high confidence.
Efficient framework for training machine learning models at edge without data movement.
problem Lack of privacy-preserving and computationally efficient methods for deep learning model training.
method Privacy preserving FedCollabNN framework for federated learning.
result Framework is computationally efficient and robust against adversarial attacks.
Paper proposes protecting DNN models with secret key preprocessing.
problem Protecting deep learning models from unauthorized access.
method Block-wise pixel shuffling with secret key for preprocessing.
result Protected models maintain close performance to non-protected models with correct key, but accuracy drops significantly with incorrect key.
FaceSigns embeds a secret watermark in images to authenticate and detect deepfakes.
problem Realistic image and video manipulation threats, especially deepfakes.
method Semi-fragile watermarking using neural networks, robust to face-swapping but fragile to deepfake manipulations.
result FaceSigns can reliably detect deepfake content with high accuracy.
Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to the corresponding labels while ensuring generalizability to unseen data. However, they do not integrate meaning-based relationships among la…
InstaHide encrypts images for privacy in distributed learning.
problem Private training of deep neural networks on distributed data.
method Encryption of training images with one-time secret keys and pixel-wise masks.
result Preserves privacy with minor accuracy loss, secure against known attacks.
New insights link diverse statistical problems via secret leakage planted clique.
problem Statistical-computational gaps in inference problems.
method Secret leakage planted clique as a new hardness assumption for reductions.
result Establishes tight statistical-computational tradeoffs for various problems.
Adversarial training was recently shown to be competitive against supervised learning methods on computer vision tasks, however, studies have mainly been confined to generative tasks such as image synthesis. In this paper, we apply adversarial training techniques to the discriminative task of learning a steganographic …
Novel strategy for federated learning with privacy-preserving predictors and nonvacuous generalization bounds.
problem Privacy-preserving federated learning with nonvacuous generalization bounds.
method Randomized predictors, PAC-Bayesian generalization bound, synchronous and heterogeneous/homogenous cases.
result Achieves comparable predictive performance to batch approach while preserving privacy.
Paper evaluates and mitigates privacy risks in deep learning models.
problem Quantifying and defending against privacy attacks in deep learning.
method Quantitative evaluation of trade-offs, reformulating attacks, and proposing a novel SPN.
result Model accuracy improved by 5-20% while maintaining data privacy.
Privacy improves robustness in statistical estimation.
problem Sparse mean estimation under privacy constraints.
method Sum-of-Squares method and exponential-time mechanisms.
result Private algorithms matching optimal tradeoffs are not known, but achieved via Sum-of-Squares.
Generative Adversarial Networks (GANs) have been used to model the underlying probability distribution of sample based datasets. GANs are notoriuos for training difficulties and their dependence on arbitrary hyperparameters. One recent improvement in GAN literature is to use the Wasserstein distance as loss function le…
Poisoning datasets can reveal private details of other users' training points.
problem Integrity and privacy of machine learning training data.
method Active inference attacks that poison a small fraction of the training dataset.
result Poisoning as little as 0.1% of the training dataset can significantly boost inference attacks.
Paper improves privacy for language models against reconstruction attacks.
problem Reconstruction attacks can regenerate training data from language models.
method Uses Rényi differential privacy with optimized privacy budgets.
result Better privacy guarantees for extraction of rare secrets.
In the last decade, deep learning algorithms have become very popular thanks to the achieved performance in many machine learning and computer vision tasks. However, most of the deep learning architectures are vulnerable to so called adversarial examples. This questions the security of deep neural networks (DNN) for ma…
Decor protects decentralized learning models from curious users.
problem Privacy violation in decentralized learning.
method Decor uses correlated Gaussian noises to protect local models in decentralized SGD with differential privacy guarantees.
result Decor matches central DP optimal privacy-utility trade-off for arbitrary connected graphs.
Survey solves curvature problems with hyperbolic spaces.
problem Singularities in hypersurface geometry.
method Hyperbolic unfolding correspondence linking hypersurfaces to Gromov hyperbolic spaces.
result Eliminates hypersurface singularities in scalar curvature geometry.
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In par…
Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on end devices. However, such convenience comes with a cost of privacy because users have to upload their private data to the cloud. This research aims to provide effective and efficient MLaaS such that the cloud server learns nothing …
Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a d…
Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on mo…
Researchers found PP-GANs can hide sensitive data in sanitized images, undermining privacy checks.
problem Lack of formal proofs of privacy in PP-GANs for image sanitization.
method Subverted PP-GANs for facial expression recognition to hide sensitive data in sanitized images.
result It is possible to hide sensitive identification data in sanitized PP-GAN output images, even allowing reconstruction of entire input images.
Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning setting. In this paper, we focus on horizontal federated learning, where data sa…
The method of moving frames in Lie sphere geometry has produced significant results in the classification of Dupin hypersurfaces in spheres. What is the secret of its effectiveness? The answer emerges in the classification of nonumbilic isoparametric surfaces in the space form geometries. Using the method of moving fra…
Today, large amounts of valuable data are distributed among millions of user-held devices, such as personal computers, phones, or Internet-of-things devices. Many companies collect such data with the goal of using it for training machine learning models allowing them to improve their services. User-held data is, howeve…
Optimal risk sharing without convex preferences using aggregate convexity.
problem Risk sharing among non-convex preferences.
method Aggregate convexity principles and Lyapunov convexity, combined with approximation arguments for law invariant risk measures.
result Derivation of a computationally tractable formula for the conjugate of the value function.
Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and …
Paper finds a method to compute fair risk-sharing rules.
problem Finding a fair and understandable risk-sharing rule.
method Established a one-to-one correspondence with a fixed point approach.
result Fast numerical method for computing AFPO risk-sharing rules.
There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One approach to resolving this is a shared response factor model. This assumes a shared and time synchronized stimulus across subjects. Such a model…
The paper proposes methods to find a shared active subspace for multivariate vector-valued functions.
problem Minimizing the deviation between function evaluations in the original and reconstructed spaces.
method Manipulating gradients or SPD matrices to identify a shared structure.
result Summing SPD matrices often identifies the best shared active subspace.
This paper aims to explore the mechanical effect of a company's share repurchase on earnings per share (EPS). In particular, while a share repurchase scheme will reduce the overall number of shares, suggesting that the EPS may increase, clearly the expenditure will reduce the net earnings of a company, introducing a tr…
New model extracts shared brain activity patterns from fMRI data.
problem Challenges in aggregating multi-subject fMRI data due to variability.
method Shared Gaussian Process Factor Analysis (S-GPFA) incorporating temporal information.
result Model reveals ground truth latent structures and replicates experimental performance.