arXiv research

Multi-party machine learning leaks global dataset properties even with black-box access.

Protocol minimizes disclosure in classification tasks.

Private method measures nonlinear correlations between data hosted across two entities.

Study tackles RLHF with diverse human feedback, showing limitations and proposing a meta-learning approach.

This paper applies secure multi-party computation to K-means clustering to protect private data.

Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm. They use the reward as a learning signal and aim to maximize the total reward over the inputs. Contextual bandits are commonly used to solve recommendation or ranking problems. This paper considers a learning s…

Securely share encrypted data for machine learning training.

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 $k\in[m]$ of the parties, and for each cor…

We detail distributed algorithms for scalable, secure multiparty linear regression and feature selection at essentially the same speed as plaintext regression. While the core geometric ideas are simple, the recognition of their broad utility when combined is novel. Our scheme opens the door to efficient and secure geno…

This paper analyzes privacy-preserving methods for collaborative forecasting.

This paper optimizes SMPC for neural network inference, reducing memory and time.

Efficiently preserves privacy in logistic regression for IoT data.

Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must…

FLFE improves machine learning by efficiently and securely transforming features.

Secure XGB for privacy-preserving machine learning in federated learning.

Securely evaluates the benefits of merging datasets for causal estimation.

How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed w…

HDP-VFL hybridizes DP for VFL, reducing privacy costs.

Asynchronous federated learning for vertically partitioned data improves efficiency and privacy.

Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation mo…

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…

Dialogue act (DA) classification has been studied for the past two decades and has several key applications such as workflow automation and conversation analytics. Researchers have used, to address this problem, various traditional machine learning models, and more recently deep neural network models such as hierarchic…

Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfe…

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…

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 …

Machine learning (ML) over distributed multi-party data is required for a variety of domains. Existing approaches, such as federated learning, collect the outputs computed by a group of devices at a central aggregator and run iterative algorithms to train a globally shared model. Unfortunately, such approaches are susc…

This paper examines anomalies and frauds in blockchain networks and proposes detection techniques.

A new Fusion method combines multiple distributions efficiently.

A novel approach to federated learning with strong privacy guarantees.

Novel simplex-valued distribution improves on existing models.

Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encr…

Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy. For instance, the OpenMalaria f…

Paper proposes scalable privacy-preserving DNN for industrial applications.

Paper develops methods for fair insurance pricing without direct access to sensitive attributes.

Framework for AI healthcare products from concept to market.

Paper introduces a method for generating interlocutor-aware facial gestures in dyadic settings.

Secure federated learning reduces privacy risks with differential privacy and secure multiparty computation.

SOTERIA optimizes neural networks for secure inference with minimal overhead.

PBM mechanism improves privacy and accuracy in federated learning.

Masked LARk prevents cross-site tracking while training models.