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.
This paper detects function-level obfuscation in binary code using graph-based methods.
Paper proposes a framework to identify and obfuscate sensitive features via information density estimation.
New method detects malware through obfuscation using adversarial risk analysis.
We inject undetectable backdoors into obfuscated neural networks and language models.
The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data. We introduce an idea for a privacy-preserving transformation on natural language data, inspired by homomorphic encryption. Our primary tool is {\em obfusc…
New flaw found in SAP defense, reducing its effectiveness to 0.1%.
We consider the problem of obfuscating sensitive information while preserving utility, and we propose a machine learning approach inspired by the generative adversarial networks paradigm. The idea is to set up two nets: the generator, that tries to produce an optimal obfuscation mechanism to protect the data, and the c…
Paper presents a lightweight, unobtrusive method to protect edge device data privacy.
The paper explores maximal perturbations to hide certain attributes in data while keeping the model's performance intact.
It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function w…
Improves code2vec for Java classes by obfuscating variable names.
Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices. Offloading the computations to the cloud can circumvent these constraints but introduces a privacy risk since the operator of the cloud is not necessarily trustworthy.…
Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share. Various methods have been proposed to minimize the potential information leakage of sensitive attributes while maximizing the task accuracy. However, little is known about the theory b…
Deep learning models misclassify malware with added benign features.
The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches develope…
Adversarial training is an effective methodology for training deep neural networks that are robust against adversarial, norm-bounded perturbations. However, the computational cost of adversarial training grows prohibitively as the size of the model and number of input dimensions increase. Further, training against less…
LLMs detect market patterns through causal reasoning, not just temporal association.
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
This work enhances collaborative inference privacy by minimizing conditional entropy and boosting robustness against model inversion attacks.
New defense method inspired by encryption improves visual classification accuracy.
RENNs protect input privacy by rotating d-ary features.
Additive noise protects privacy in releasing datasets for SVM classification.
Paper defends sensitive attributes in GNNs from inference attacks.
Paper proposes a method to encrypt faces while maintaining visual similarity.
In this work we revisit gradient regularization for adversarial robustness with some new ingredients. First, we derive new per-image theoretical robustness bounds based on local gradient information. These bounds strongly motivate input gradient regularization. Second, we implement a scaleable version of input gradient…
Simplifies fair PCA with fast, efficient solution.
Generative Adversarial Network purifies images from steganography without degrading quality.
The authors seek financial datasets to benchmark feature engineering methods on US market data.
With the celebrated success of deep learning, some attempts to develop effective methods for detecting malicious PowerShell programs employ neural nets in a traditional natural language processing setup while others employ convolutional neural nets to detect obfuscated malicious commands at a character level. While the…
SONet stabilizes ODE networks for robustness without adversarial training.
Recurrent neural networks (RNNs) are powerful constructs capable of modeling complex systems, up to and including Turing Machines. However, learning such complex models from finite training sets can be difficult. In this paper we empirically show that RNNs can learn models of computer peripheral devices through input a…
Origami uses SGX enclaves and blinding to protect deep neural network inference privacy.
New mechanisms from primate vision improve neural network robustness.
Identifying meaningful signal buried in noise is a problem of interest arising in diverse scenarios of data-driven modeling. We present here a theoretical framework for exploiting intrinsic geometry in data that resists noise corruption, and might be identifiable under severe obfuscation. Our approach is based on uncov…
MatchGNet detects malware by learning program behavior graphs.
Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities. However, they have not been originally devised to deal with deliberate attack attempts that may aim to subvert the clustering process itself. Whether clustering can be safely adopted in such settings r…
Secure neural network inference on untrusted platforms using holographic reduced representations.
New methods interpret clustering outcomes without altering data structure.
Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existing work on the problem operates under the assumption that the sensitive feature available in one's training sample is perfectly reliable. This assumption may be violated in…
Deep Neural Networks (DNNs) have recently led to significant improvements in many fields. However, DNNs are vulnerable to adversarial examples which are samples with imperceptible perturbations while dramatically misleading the DNNs. Moreover, adversarial examples can be used to perform an attack on various kinds of DN…
Financial statements contain quantitative information and manager's subjective evaluation of firm's financial status. Using information released in U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built…
We develop a model for contagion in reinsurance networks by which primary insurers' losses are spread through the network. Our model handles general reinsurance contracts, such as typical excess of loss contracts. We show that simpler models existing in the literature--namely proportional reinsurance--greatly underesti…
New framework promotes reproducible, domain-agnostic reinforcement learning algorithms.
Paper presents privacy-preserving techniques for HD computing.
Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an anonymous author even when the author tries to remove privacy sensitive content. In this paper, we propose an automatic method, called Adver…
ADAPT improves robustness of Vision Transformers without full model fine-tuning.
Study examines biases in clinical word embeddings, revealing performance gaps across groups.
Cellular regulatory dynamics is driven by large and intricate networks of interactions at the molecular scale, whose sheer size obfuscates understanding. In light of limited experimental data, many parameters of such dynamics are unknown, and thus models built on the detailed, mechanistic viewpoint overfit and are not …