arXiv research

New model values equity-linked securities with guaranteed return.

RL models improve target control in SSGs for security applications.

Securely trains regression models with secret sharing for data collaboration.

Most of the existing solutions to enterprise threat management are preventive approaches prescribing means to prevent policy violations with varying degrees of success. In this paper we consider the complementary scenario where a number of security violations have already occurred, or security threats, or vulnerabiliti…

Study assesses how much security restaking protocols need to pay for.

Proposes a secure communication method independent of eavesdropper's decoder.

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

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

Securely share encrypted data for machine learning training.

Explores security challenges of machine learning in real-world systems.

Sparse oblique decision tree improves security rules for renewable power systems.

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…

The study calculates securities lending haircuts and indemnification costs.

Secure federated learning framework resists adversarial users.

We propose a new randomized ensemble technique with a provable security guarantee against black-box transfer attacks. Our proof constructs a new security problem for random binary classifiers which is easier to empirically verify and a reduction from the security of this new model to the security of the ensemble classi…

Paper revisits PCA for anomaly detection in network security.

Wrist movements can reveal digits, posing security risks.

Optimizes a portfolio for an investor preferring accepted securities over a reference security.

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

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

Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-Itô-OI model, we assume that the option-implied…

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

FastSecAgg improves federated learning security and efficiency.

This research secures deployed sentiment analysis models by identifying and defending against attack vectors.

Study shows Bitcoin security tied to mining rewards and prices.

Contextual bandit framework improves revenue optimization in securities lending market.

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 …

A pair of points in a riemannian manifold makes a secure configuration if the totality of geodesics connecting them can be blocked by a finite set. The manifold is secure if every configuration is secure. We investigate the security of compact, locally symmetric spaces.

New model improves classifier security against evasion attacks by selecting features.

Market-based portfolio variance measures risks using trade data.

We say that a pair of points x and y is secure if there exist a finite set of blocking points such that any geodesic between x and y passes through one of the blocking points. The main point of this paper is to exhibit new examples of blocking phenomena both in the manifold and the billiard table setting. As an approac…

The ever-growing big data and emerging artificial intelligence (AI) demand the use of machine learning (ML) and deep learning (DL) methods. Cybersecurity also benefits from ML and DL methods for various types of applications. These methods however are susceptible to security attacks. The adversaries can exploit the tra…

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

Study proposes hybrid machine learning models for crop yield prediction.

Secure SMMD enables data privacy in federated learning.

PrivacyFL simulates privacy-preserving federated learning.

The article presents a new entropy model for assessing stock market interest.

This research highlights the secrecy potential of nonlinear generative models and their all-or-nothing phase transition.

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…

Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descen…

A riemannian manifold is secure if the geodesics between any pair of points in the manifold can be blocked by a finite number of point obstacles. Compact, flat manifolds are secure. A standing conjecture says that these are the only secure, compact riemannian manifolds. The conjecture claims, in particular, that a riem…

Paper develops security model and pricing for stable digital currency in quantum blockchain network.

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

Markowitz simplified portfolio returns assuming constant trade volumes.

Paper proposes a secure protocol for federated learning.

A system for federated learning with private data, adding discrete Gaussian noise and secure aggregation.

Secure aggregation for buffered asynchronous federated learning without TEEs.

To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compromise its integrity and efficiency. Security attacks can be divided bas…