Paper proposes a framework to identify and obfuscate sensitive features via information density estimation.
problem Identifying and protecting sensitive attributes from leakage in obfuscation mechanisms.
method Information density estimation to identify leaking features, followed by a targeted obfuscation mechanism.
result Proven leakage guarantee in terms of Eγ-divergence for the obfuscation mechanism. 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…
This paper detects function-level obfuscation in binary code using graph-based methods.
problem Detecting and characterizing function-level obfuscation in binary code.
method Graph-based approaches, including GNNs, are compared on various datasets.
result GNNs outperform baselines in function-level obfuscation detection, especially in a 11-class classification task.
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.
problem Protecting inference data privacy in IoT edge devices with limited compute power.
method A lightweight neural network at edge devices to obfuscate inference data without indicating obfuscation.
result Effectively protects inference data confidentiality while preserving backend accuracy.
The paper explores maximal perturbations to hide certain attributes in data while keeping the model's performance intact.
problem Protecting sensitive attributes from both model and human detection.
method Adversarial perturbations applied to raw data to conditionally damage model's classification of one attribute while preserving the rest.
result Maximal perturbations can hide certain attributes from both model and human detection, impacting model performance but not human perception.
New method detects malware through obfuscation using adversarial risk analysis.
problem Insufficient malware detection in obfuscated binaries.
method Hybrid framework and adversarial risk analysis.
result Improved detection of malware through obfuscation.
We inject undetectable backdoors into obfuscated neural networks and language models.
problem Safeguarding models from sophisticated adversarial attacks.
method Developed a strategy to plant undetectable backdoors in obfuscated neural networks and language models.
result Undetectable backdoors can be planted in obfuscated models, even if weights and architecture are accessible.
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.…
New flaw found in SAP defense, reducing its effectiveness to 0.1%.
problem Weakness in Stochastic Activation Pruning defense against adversarial attacks.
method Re-examined the implementation of SAP and introduced a new BPDA attack.
result SAP's effectiveness reduced to 0.1% when properly applied.
Improves code2vec for Java classes by obfuscating variable names.
problem Code2vec's reliance on variable names makes it vulnerable to typos and attacks.
method Obfuscate variable names during code2vec training and aggregate method embeddings for class-level predictions.
result Obfuscated variable names improve model's robustness and accuracy.
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…
Paper discusses privacy issues in IoT and proposes a lightweight neural network approach.
problem Privacy concerns in IoT due to extensive data collection and processing.
method Developed a privacy-preserving inference approach for IoT objects and a deep neural network in the cloud.
result Satisfactory performance of the proposed approach on the MNIST dataset.
Deep learning models misclassify malware with added benign features.
problem Detecting malware with deep learning when it's mixed with benign code.
method Trained a deep neural network classifier using benign and malware features. Demonstrated the impact of adding benign features to malware. Used data augmentation to improve classifier robustness.
result Adding benign features to malware significantly increases false negatives.
NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.
problem Privacy-preserving data collaboration with reduced reconstruction risk.
method Formulates linear kernel integration, kernelizes it, and introduces graph regularization and centering constraints.
result NKI improves classification accuracy over existing linear integration methods under nonlinear dimensionality reduction.
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…
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.
problem Detecting structural market patterns in financial data.
method Obfuscation testing using the WHO-WHOM-WHAT framework.
result LLMs achieve 71.5% detection rate of market patterns without temporal context.
This work enhances collaborative inference privacy by minimizing conditional entropy and boosting robustness against model inversion attacks.
problem Privacy leakage in collaborative inference systems via model inversion attacks.
method Theoretical proof and derivation of a differentiable measure for bounding conditional entropy, followed by a CEM algorithm to maximize it.
result Theoretical proof and experimental validation show that CEM consistently boosts inversion robustness without compromising feature utility or efficiency.
Paper proposes a method to encrypt faces while maintaining visual similarity.
problem Protecting personal data from unauthorized face recognition.
method Targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks.
result TIP-IM provides 95%+ protection success rate against face recognition models.
Additive noise protects privacy in releasing datasets for SVM classification.
problem Maintaining privacy in releasing datasets for SVM classification.
method Additive noise applied to obfuscate the dataset, optimizing privacy and utility measures.
result Optimal noise distribution ensures close classifier performance between original and obfuscated datasets, achieving local differential privacy.
Paper defends sensitive attributes in GNNs from inference attacks.
problem Protecting sensitive attributes in GNNs from inference attacks.
method Proposes adversarial training with TV and Wasserstein distance to locally filter sensitive attributes.
result Framework creates strong defense against inference attacks with minimal performance loss.
The authors seek financial datasets to benchmark feature engineering methods on US market data.
problem Improving predictive models for financial data science competitions.
method Feature engineering methods applied to multivariate time-series data from the US market.
result Predictive power of models tested against Numerai-Signals targets.
New defense method inspired by encryption improves visual classification accuracy.
problem Conventional defenses reduce accuracy and are defeated by obfuscated gradients.
method Block-wise pixel shuffling with secret key for training and test images.
result Achieves high accuracy (91.55%) on clean images and (89.66%) on adversarial examples.
RENNs protect input privacy by rotating d-ary features.
problem Protecting input privacy from intermediate-layer features.
method Rotation-equivariant neural networks using d-ary vectors/tensors.
result RENNs effectively hide input information without degrading output accuracy.
Simplifies fair PCA with fast, efficient solution.
problem Learning fair low-rank approximations of data.
method Conceptually simple approach with analytic solution.
result Faster and similar results to existing fair PCA methods.
Origami uses SGX enclaves and blinding to protect deep neural network inference privacy.
problem Protecting deep neural network inference privacy in machine learning services.
method Combines enclave execution, cryptographic blinding, and accelerator-based computation.
result Demonstrates improved privacy-preserving inference performance compared to prior work.
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…
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…
New methods interpret clustering outcomes without altering data structure.
problem Post-processing methods destroy data integrity and obscure interpretations.
method Algorithm-agnostic interpretation methods using permutation feature importance, individual conditional expectation, and partial dependence.
result Preserves original feature structure and explains clustering outcomes.
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…
Generative Adversarial Network purifies images from steganography without degrading quality.
problem Destruction of image steganography while maintaining visual quality.
method Generative Adversarial Network (GAN) optimized for steganography destruction.
result High rate of steganographic content destruction with minimal visual quality degradation.
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 …
Paper examines GCHP for mid-price prediction in financial data.
problem Predicting mid-price in financial data with stochastic models.
method General Compound Hawkes Process (GCHP) for mid-price prediction.
result GCHP models show potential for predicting mid-price direction and volatility.
Federated learning algorithm reduces global model size by combining local and global representations.
problem Scalability issues in training large models on private data distributed over multiple devices.
method Proposes a federated learning algorithm that jointly learns compact local representations and a global model.
result The global model can be smaller since it only operates on local representations, reducing the number of communicated parameters.
This work proposes a novel privacy-preserving method for synthetic replacement of sensitive data.
problem Privacy-preserving transformations of sensitive data.
method Adversarial representation learning for synthetic replacement of private attributes.
result Our method provides stronger privacy and better utility than previous methods.
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.
problem Improving adversarial robustness of neural networks without sacrificing natural accuracy.
method SONet uses skew-symmetric ODE blocks and DOPRI5 solver for robustness.
result SONet achieves comparable robustness to adversarial defense methods without trade-off.
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…
In large-scale statistical learning, data collection and model fitting are moving increasingly toward peripheral devices---phones, watches, fitness trackers---away from centralized data collection. Concomitant with this rise in decentralized data are increasing challenges of maintaining privacy while allowing enough in…
Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model'…
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…
Paper presents privacy-preserving techniques for HD computing.
problem Privacy loss in HD computing due to reversible computation.
method Quantization and pruning of hypervectors for differential privacy.
result Differentially private HD model for cloud inference.
A novel supervised visualization technique for data exploration.
problem Lack of supervised dimensionality reduction methods considering class labels.
method Random forest proximities and diffusion-based dimensionality reduction.
result Retains local and global structures in data, emphasizing important variables.
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…
New mechanisms from primate vision improve neural network robustness.
problem Demonstrating robust neural networks to small adversarial perturbations.
method Investigated two biologically plausible mechanisms: non-uniform retina sampling and receptive field diversity.
result Non-uniform retina sampling and receptive field diversity improve adversarial robustness.
Bayesian framework for model uncertainty identifies complex heterogeneity without strong assumptions.
problem Identifying complex heterogeneity in factorial data with varying covariates.
method Rashomon Partition Sets (RPS) using l0 prior for robust model uncertainty.
result RPS provides a robust set of models capturing complex heterogeneity without strong assumptions.
MatchGNet detects malware by learning program behavior graphs.
problem Malware evasion through obfuscation and high false positives in traditional detection methods.
method Heterogeneous Graph Matching Network model that learns graph representation and similarity metrics.
result MatchGNet reduces false positives by 50% while maintaining zero false negatives.