arXiv research

Securely share encrypted data for machine learning training.

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

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

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

Efficiently preserves privacy in logistic regression for IoT data.

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

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…

Securely evaluates the benefits of merging datasets for causal estimation.

This paper analyzes privacy-preserving methods for collaborative forecasting.

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

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…

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…

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…

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…

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…

Paper proposes scalable privacy-preserving DNN for industrial applications.

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

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 …

FLFE improves machine learning by efficiently and securely transforming features.

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,…

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

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…

SOTERIA optimizes neural networks for secure inference with minimal overhead.

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…

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…

Masked LARk prevents cross-site tracking while training models.

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.

PBM mechanism improves privacy and accuracy in federated learning.

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

Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developing methods for explaining the predictions of neural networks. While sev…

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…

To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing …

Secure neural network inference on untrusted platforms using holographic reduced representations.

As neural networks revolutionize many applications, significant privacy conflicts between model users and providers emerge. The cryptography community developed a variety of techniques for secure computation to address such privacy issues. As generic techniques for secure computation are typically prohibitively ineffec…

The exponential increase in dependencies between the cyber and physical world leads to an enormous amount of data which must be efficiently processed and stored. Therefore, computing paradigms are evolving towards machine learning (ML)-based systems because of their ability to efficiently and accurately process the eno…

We develop a secure aggregation protocol for federated learning that reduces communication and computation costs.

Secure Multiparty Computation protects data privacy in Symbolic Regression.

SLIP secures LLMs on edge devices by splitting computation and protecting sensitive parts.

FastSecAgg improves federated learning security and efficiency.

Securely trains neural networks remotely with deep learning's flaws.

QFNN-FFD uses quantum computing and FL for secure financial fraud detection.

Securely trains regression models with secret sharing for data collaboration.

Paper proposes a new method to compute cryptocurrency prices securely.

Post-Quantum Secure Federated DeFi for Inclusive Banking

Thanks to the advances in machine learning, data-driven analysis tools have become valuable solutions for various applications. However, there still remain essential challenges to develop effective data-driven methods because of the need to acquire a large amount of data and to have sufficient computing power to handle…

The study calculates securities lending haircuts and indemnification costs.