arXiv research

Securely trains regression models with secret sharing for data collaboration.

Securely share encrypted data for machine learning training.

Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architectures that guarantee preservation of data privacy, trustworthy sequence of iterative learning and equ…

FastSecAgg improves federated learning security and efficiency.

The paper studies an oligopolistic equilibrium model of financial agents who aim to share their random endowments. The risk-sharing securities and their prices are endogenously determined as the outcome of a strategic game played among all the participating agents. In the complete-market setting, each agent's set of st…

We consider the risk sharing problem for capital requirements induced by capital adequacy tests and security markets. The agents involved in the sharing procedure may be heterogeneous in that they apply varying capital adequacy tests and have access to different security markets. We discuss conditions under which there…

A new federated learning method protects privacy in mobile crowdsensing.

The large majority of risk-sharing transactions involve few agents, each of whom can heavily influence the structure and the prices of securities. This paper proposes a game where agents' strategic sets consist of all possible sharing securities and pricing kernels that are consistent with Arrow-Debreu sharing rules. F…

Study examines reasons for Nutek India's share price drop.

Paper tackles multiplayer symmetric games, securing equal share for n players.

The paper analyzes security issues in blockchain ecosystems with multiple SSPs and proposes two models for better stake management.

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…

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

This study assesses how share capital affects financial growth of non-financial firms listed at NSE.

Unified market-based description of returns and variances of trades.

Broker uses multi-task dynamic pricing to learn competitive prices in credit markets.

Proposes a method for private aggregation in heterogeneous federated learning.

Turbo-Aggregate reduces secure aggregation time from quadratic to nearly linear.

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

Chebyshev polynomials analyze Czech enterprises' stock dynamics.

Market-based portfolio variance measures risks using trade data.

Federated learning leaks participant dataset quality even with secure aggregation.

Study shows long-term debt impacts financial growth of non-financial firms listed at Nairobi Securities Exchange.

Collaborative (federated) learning enables multiple parties to train a model without sharing their private data, but through repeated sharing of the parameters of their local models. Despite its advantages, this approach has many known privacy and security weaknesses and performance overhead, in addition to being limit…

Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospi…

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 …

Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how …

Paper tackles cybersecurity attack detection with an ensemble approach.

Study assesses short-term debt's impact on non-financial firms' financial growth.

This paper analyzes privacy-preserving methods for collaborative forecasting.

A privacy-preserving framework detects faults in circular economy processes.

Study on InstaHide's security, linking to phase retrieval problem.

Framework allows organizations to collaborate on learning tasks securely.

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…

PrivacyFL simulates privacy-preserving federated learning.

Research shows collective learning across diverse environments is hard due to privacy and security concerns.

Paper proposes scalable privacy-preserving DNN for industrial applications.

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 …

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…

This paper analyzes how training data can be leaked from gradients in neural networks and proposes a metric for measuring model security.

Cloud computing is gaining significant attention, however, security is the biggest hurdle in its wide acceptance. Users of cloud services are under constant fear of data loss, security threats and availability issues. Recently, learning-based methods for security applications are gaining popularity in the literature wi…

Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on …

At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, can…

In this article I describe a research agenda for securing machine learning models against adversarial inputs at test time. This article does not present results but instead shares some of my thoughts about where I think that the field needs to go. Modern machine learning works very well on I.I.D. data: data for which e…

Markowitz simplified portfolio returns assuming constant trade volumes.

Model improves fraud detection for new scenes with limited data.

We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gr…

Efficient privacy-preserving machine learning framework using random transformations.