This paper explores security threats in ML systems and proposes mitigation techniques.
problem Security vulnerabilities in ML-based systems during training and inference.
method Overview of security threats, demonstrations using LeNet and VGGNet, proposed attack.
result Demonstrated security threats and proposed mitigation techniques.
Framework for privacy-preserving deep learning using secure data processing.
problem Ensuring privacy in deep learning models while maintaining functionality.
method Introduces a generic framework that prioritizes data ownership and secure processing, using chains of commands and tensors.
result Early results show no impact on prediction accuracy but introduce a significant performance overhead.
The DoD needs a robust process to evaluate AI/ML model performance and robustness.
problem AI/ML models are brittle and nonrobust, posing risks in national security.
method Reviews AI/ML development process and best practices for evaluation.
result Recommendations for DoD evaluators to ensure robust AI/ML capabilities.
Paper proposes a recursive PLS model for optimal response to security threats.
problem Optimal response to security threats after violations have occurred.
method Recursive Partial Least Squares (PLS) model with factorial analysis of security events.
result The model optimally estimates security administrators' responses to threats.
In this paper incomplete-information models are developed for the pricing of securities in a stochastic interest rate setting. In particular we consider credit-risky assets that may include random recovery upon default. The market filtration is generated by a collection of information processes associated with economic…
Study on how changing curvature of GPC affects different types of attacks.
problem Understanding the relationship between different test-time attacks on GPCs.
method Examined Gaussian Process classifiers and their vulnerability to various attacks.
result Changing curvature of GPCs can make it robust against some attacks but vulnerable to others.
Study optimal portfolio selection in a complex market with jumps and regime shifts.
problem Optimal portfolio selection in a market with jumps and regime shifts.
method Modeling a market with Lévy processes and regime switching, using various securities to complete the market, solving the portfolio selection problem for power and logarithmic utilities.
result Conditions for asymptotic-arbitrage-free market and solutions for optimal portfolio selection.
This paper reviews security and privacy issues in deep learning models.
problem Security and privacy threats in deep learning models.
method Analysis of security and privacy attacks, including poisoning and evasion attacks, and proposed defenses. Also, review of data privacy threats and solutions.
result Various security and privacy attacks and their defenses are discussed.
K-Metamodes clusters security data without converting categorical attributes.
problem Clustering heterogeneous security data sets with categorical and numerical attributes.
method Frequency-based distance function for ensemble-based k-modes clustering, adapted feature discretisation.
result Higher effectiveness compared to previous methods on public security data sets.
Paper simplifies default process modeling and credit valuation.
problem Modeling and pricing derivative securities with credit risk.
method Integrates default process, probability, and correlation into a unified framework.
result Risky valuation is Martingale in the proposed model.
This work assesses the security of clustering algorithms against adversarial attacks.
problem The security of clustering algorithms in adversarial settings is questionable.
method Proposes a framework to identify and evaluate attacks on clustering algorithms.
result Attackers can significantly poison clustering processes with a small percentage of attack samples.
Biscotti uses blockchain for secure peer-to-peer ML without central coordination.
problem Secure and private multi-party machine learning with trust and poisoning attacks.
method Decentralized peer-to-peer ML using blockchain and cryptography.
result Biscotti protects privacy and model performance against 30% adversaries.
Unified framework for pricing various debt securities.
problem Pricing of different types of debt securities under general short-rate processes.
method Unifying framework using continuous-time Markov chain approximations and bi-dimensional diffusion processes.
result Closed-form matrix expressions and efficient algorithms for pricing various debt securities.
HASP enhances mobile security against ASR by adding imperceptible noise.
problem Security leakage from ASR on mobile devices.
method Adversarial noise to perturb WER and enhance security.
result HASP reduces WER by 84.55% and speeds up data processing.
A privacy-preserving framework detects faults in circular economy processes.
problem Lack of shared data across company borders due to privacy concerns.
method Federated Principal Component Analysis (PCA) and Secure Multiparty Computation.
result The proposed FedMSPC framework outperforms standard PCA in fault detection.
Research analyzes ethical concerns around MEV on blockchain and social media.
problem Fairness issues in transaction ordering on blockchain.
method Applied NLP methods to analyze topics in tweets on MEV.
result Tweets discussed ethical concerns like security, equity, and solutions to MEV.
Secure federated learning framework resists adversarial users.
problem Resilience against adversarial (Byzantine) users in federated learning.
method Integrated stochastic quantization, verifiable outlier detection, and secure model aggregation.
result First single-server Byzantine-resilient secure aggregation framework (BREA) for secure federated learning.
Paper revisits PCA for anomaly detection in network security.
problem Understanding and improving anomaly detection in network security.
method Revisit probabilistic PCA model and its connection to MSNM framework.
result Mathematical model connects PCA to MSNM for anomaly detection.
The paper analyzes corporate security prices in incomplete credit risk models.
problem Computing the price dynamics of traded securities in models with unobservable firm asset values.
method Transformed the stochastic filtering problem for the asset value into a filtering problem for a stopped diffusion process and applied filtering literature results.
result Obtained an SPDE-characterization for the filter density and determined the price dynamics of traded securities.
In an incomplete continuous-time securities market with uncertainty generated by Brownian motions, we derive closed-form solutions for the equilibrium interest rate and market price of risk processes. The economy has a finite number of heterogeneous exponential utility investors, who receive partially unspanned income …
This research develops secure DSVM algorithms using game theory.
problem Vulnerability of DSVM in adversarial environments.
method Game-theoretic framework to model conflicting interests between adversary and DSVM units.
result Guaranteed convergence of distributed learning algorithms without data or network topology assumptions.
The study values a new type of insurance-linked security called CocoCat bonds.
problem Valuing a new type of insurance-linked security called contingent convertible catastrophe bonds.
method Formalized design, derived analytical valuation formulae, used time-inhomogeneous compound Poisson process for natural catastrophe losses, and applied exponential change of measure and Girsanov-like transformation.
result CocoCat bond prices are most sensitive to interest rates, conversion fractions, and trigger levels.
We consider a class of generalized capital asset pricing models in continuous time with a finite number of agents and tradable securities. The securities may not be sufficient to span all sources of uncertainty. If the agents have exponential utility functions and the individual endowments are spanned by the securities…
Proposes a method for private aggregation in heterogeneous federated learning.
problem Ensuring resilience to Byzantine clients and maintaining client data privacy in federated learning with heterogeneous data.
method Careful co-design of verifiable secret sharing, secure aggregation, and private information retrieval scheme.
result Achieves information-theoretic privacy guarantees and Byzantine resilience under data heterogeneity.
We prove a version of First Fundamental Theorem of Asset Pricing under transaction costs for discrete-time markets with dividend-paying securities. Specifically, we show that the no-arbitrage condition under the efficient friction assumption is equivalent to the existence of a risk-neutral measure. We derive dual repre…
Deep neural networks detect cyberthreats from Twitter.
problem Scalable processing of cybersecurity information from social media.
method Convolutional and bidirectional LSTM networks for tweet classification and named entity recognition.
result Pipeline achieves high true positive and F1-score rates for threat detection.
PrivacyFL simulates privacy-preserving federated learning.
problem Ensuring privacy in federated learning environments.
method Extensible, configurable simulator for federated learning.
result PrivacyFL checks feasibility and improves model accuracy.
Blockchain as a Service offers a secure, decentralized computing solution.
problem Lack of transparency, security, and privacy in cloud computing.
method Decentralized cooperative computing process using blockchain, homomorphic encryption, and SDN.
result Performance evaluated via different scenarios in simulations.
This paper recovers input data from transformer models using attention weights.
problem Recovering input data from transformer models for security and privacy concerns.
method Introducing an algorithm to minimize the loss function between expected and actual outputs of transformers.
result The algorithm successfully recovers input data from attention weights and outputs of transformers.
Enhances DNNs by selectively learning key image edges.
problem Improving DNN robustness against adversarial attacks.
method Introduces Secure Selective Convolution (SSC) to learn important image edges.
result Significant reduction in attack success rate and imperceptibility of adversarial images.
Secure sum outperforms homomorphic encryption in collaborative deep learning.
problem Training deep learning models on private data from multiple parties without revealing the data.
method Used a secure sum protocol in conjunction with default secure channels.
result Secure sum protocol provides superior properties in terms of collusion-resistance and runtime.
New theory predicts security prices through a physical law, not randomness.
problem Failed attempts to understand and predict stock price evolution.
method Developed a physicomathematical theory to describe price evolution.
result Security prices are governed by a deterministic physical law, not random.
Study on price formation in a market with a major player and minor firms.
problem Equilibrium price formation in a market with a major financial firm and many minor firms.
method Analyzes the equilibrium price process in both finite and mean field models, considering idiosyncratic and common noises.
result Derives the functional form of price impact for the major firm in both market sizes.
Deep models can be fooled by maliciously crafted examples.
problem Deep learning models are vulnerable to adversarial examples that can mislead predictions without being noticed by humans.
method Reviews adversarial example generation methods and defenses, discusses their limitations and future prospects.
result Adversarial examples can mislead deep learning models without being distinguishable by humans.
HuMaINs combines human and machine strengths for better inference tasks.
problem Improving inference performance through human-machine collaboration.
method Novel signal processing and machine learning solutions for HuMaINs architecture.
result HuMaINs achieves higher performance than either humans or machines individually.
This work develops secure distributed algorithms for machine learning to protect against data poisoning and network attacks.
problem Vulnerability of distributed machine learning algorithms to cyber threats.
method Game-theoretic framework to capture conflicting goals of a learner and an attacker, iterative distributed algorithm.
result Distributed SVM is prone to fail in different types of attacks, with impact depending on network structure and attack capabilities.
Proposes a deep diffusion watermarking framework for secure image processing.
problem Secure watermarking of deep neural networks in image processing.
method End-to-end training of two FCNNs with residual structure for embedding and extraction.
result Framework improves security and robustness with customizable trade-offs.
A one-factor asset pricing model with an Ornstein--Uhlenbeck process as its state variable is studied under partial information: the mean-reverting level and the mean-reverting speed parameters are modeled as hidden/unobservable stochastic variables. No-arbitrage pricing formulas for derivative securities written on a …
The article presents a new entropy model for assessing stock market interest.
problem Assessing investor interest and market sentiment in exchange-traded securities.
method Intrinsic entropy model using actual trading data, without exogenous factors.
result Empirical evidence supports the model's ability to predict trading activity.
Study forecasts food security trends using real-time data.
problem Food insecurity prediction for sub-national regions.
method Quantitative methodology combining various machine learning models.
result Reservoir Computing model performs best in food security prediction.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
problem Investor preference for a set of securities over a reference security with constraints.
method Mean-variance optimization with Sharpe Ratio performance measurement.
result Derives an optimal portfolio that maximizes returns while minimizing risk.
MEG models for dynamic networks estimate dependencies and shared latent space relationships.
problem Modeling dynamic networks with shared latent space relationships and dependencies.
method MEG combines mutually exciting point processes and latent space models to estimate node-specific parameters and unobserved edges.
result MEG models can estimate intensities for unobserved edges, useful for anomaly detection in real-world applications.
In this paper we introduce a class of information-based models for the pricing of fixed-income securities. We consider a set of continuous- time information processes that describe the flow of information about market factors in a monetary economy. The nominal pricing kernel is at any given time assumed to be given by …
Deep neural networks improve face matching across different domains for banking security.
problem Matching facial images from ID documents with selfies for secure transactions.
method A novel deep learning architecture using two CNNs for cross-domain face matching.
result Accuracy rates higher than 93% on the FaceBank dataset.
Study shows Bitcoin security tied to mining rewards and prices.
problem Understanding Bitcoin security's dependency on market outcomes.
method Used ARDL approach with daily blockchain and Bitcoin data from 2014-2019.
result Bitcoin security outcomes linked to Bitcoin price and mining rewards.
Framework allows organizations to collaborate on learning tasks securely.
problem Limited collaboration due to security constraints.
method Assisted Learning framework for supervised learning tasks.
result Near-oracle learning performance achieved without revealing sensitive information.
This paper deals with an optimal position management problem for a market maker who has to face uncertain customer order flows in an illiquid market, where the market maker's continuous trading incurs a stochastic linear price impact. Although the execution timing is uncertain, the market maker can also ask its OTC cou…
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.