A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
RL models improve target control in SSGs for security applications.
Securely trains regression models with secret sharing for data collaboration.
SafeML monitors ML systems for safety and security risks.
This paper discusses adversarial attacks on cyber security systems using machine learning.
Article evaluates AI security threats and proposes multiple measures.
Web application security has become a major concern in recent years, as more and more content and services are available online. A useful method for identifying security vulnerabilities is black-box testing, which relies on an automated crawling of web applications. However, crawling Rich Internet Applications (RIAs) i…
Neural Networks (NN) have recently emerged as backbone of several sensitive applications like automobile, medical image, security, etc. NNs inherently offer Partial Fault Tolerance (PFT) in their architecture; however, the biased PFT of NNs can lead to severe consequences in applications like cryptography and security …
New model improves classifier security against evasion attacks by selecting features.
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…
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional me…
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…
Contextual bandit framework improves revenue optimization in securities lending market.
Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest in both research and industrial communities in the promising potentials of this paradigm. The list of current and envisioned applications of deep RL ranges from autonomous navigation and robotics to control applicatio…
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…
Investigates the cost-effectiveness of security features in smart card chips.
Secure Multiparty Computation protects data privacy in Symbolic Regression.
We introduce CheckNet, a method for secure inference with deep neural networks on untrusted devices. CheckNet is like a checksum for neural network inference: it verifies the integrity of the inference computation performed by untrusted devices to 1) ensure the inference has actually been performed, and 2) ensure the i…
Paper revisits PCA for anomaly detection in network security.
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…
SLIP secures LLMs on edge devices by splitting computation and protecting sensitive parts.
A strategy to beat benchmarks by investing in heavily shorted but fundamentally sound securities.
PoEL protocol aims to efficiently create and secure liquidity for blockchain networks.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
Secure federated learning reduces privacy risks with differential privacy and secure multiparty computation.
This paper examines anomalies and frauds in blockchain networks and proposes detection techniques.
This review covers quantum computing applications in finance and blockchain.
Secure sum outperforms homomorphic encryption in collaborative deep learning.
QFNN-FFD uses quantum computing and FL for secure financial fraud detection.
Study shows Bitcoin security tied to mining rewards and prices.
The DAO Report led to a significant shift of ICO activity to Europe.
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.
Paper proposes scalable privacy-preserving DNN for industrial applications.
This study evaluates Algorand and Ethereum 2.0 for blockchain trilemma challenges.
In this paper, we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors. ACE and ACE-KL provide insights in diagnosing which attribut…
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…
Deep-Lock secures DNN models with secret keys.
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…
A new federated learning method protects privacy in mobile crowdsensing.
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…
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…
New model values equity-linked securities with guaranteed return.
Android and Facebook provide third-party applications with access to users' private data and the ability to perform potentially sensitive operations (e.g., post to a user's wall or place phone calls). As a security measure, these platforms restrict applications' privileges with permission systems: users must approve th…
Paper proposes a secure protocol for federated learning.
GTA is the first backdoor attack on GNNs, demonstrating vulnerabilities in graph-oriented security models.
Proposes a secure communication method independent of eavesdropper's decoder.
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…
Explores security challenges of machine learning in real-world systems.