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Found 25 papers, 8 papers with code
EMP: Effective Multidimensional Persistence for Graph Representation Learning
This framework empowers the exploration of data by simultaneously varying multiple scale parameters.
Using AI Uncertainty Quantification to Improve Human Decision-Making
These results indicate that implementing high quality, instance-level UQ for AI may improve decision-making with real systems compared to AI predictions alone.
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features
PROVEXPLAINER allowed simple DT models to achieve 95% fidelity to the GNN on program classification tasks with general graph structural features, and 99% fidelity on malware detection tasks with a task-specific feature package tailored for direct interpretation.
Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain
The rise of cryptocurrencies like Bitcoin, which enable transactions with a degree of pseudonymity, has led to a surge in various illicit activities, including ransomware payments and transactions on darknet markets.
IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception
Over the years, honeypots emerged as an important security tool to understand attacker intent and deceive attackers to spend time and resources.
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT
Second, we introduce a pruning algorithm for graphs to compute their persistence diagrams by removing the dominated vertices.
The Impact of Data Distribution on Fairness and Robustness in Federated Learning
Particularly, as the data distributions of agents differ, the accuracy of the trained models drop.
Multi-concept adversarial attacks
In this paper, we address the above research question by developing novel attack techniques that can simultaneously attack one set of ML models while preserving the accuracy of the other.
Learning Generative Deception Strategies in Combinatorial Masking Games
Next, we present a novel highly scalable approach for approximately solving such games by representing the strategies of both players as neural networks.
Fair Machine Learning under Limited Demographically Labeled Data
In such a setting, we design fair training algorithms which exhibit both good utility, and low bias.
Improving Fairness of AI Systems with Lossless De-biasing
We demonstrate the effectiveness of our technique on real datasets using a variety of fairness metrics.
Smart Vectorizations for Single and Multiparameter Persistence
We derive theoretical guarantees on the stability of the new saw and multi-persistence grid functions and illustrate their applicability for graph classification tasks.
GINN: Fast GPU-TEE Based Integrity for Neural Network Training
In this work, we focus on the setting where the integrity of the outsourced Deep Learning (DL) model training is ensured by TEE.
GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous Probabilistic Integrity Verification Inside Trusted Execution Environment
Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a wide range of applications ranging from self-driving cars to COVID-19 treatment discovery.
Improving Accuracy of Federated Learning in Non-IID Settings
Particularly, in settings where local data distributions vastly differ among agents, FL performs rather poorly with respect to the centralized training.
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture
It has been shown that an attacker can inject backdoors to the trained model during FL, and then can leverage the backdoor to make the model misclassify later.
How to Not Get Caught When You Launder Money on Blockchain?
The number of blockchain users has tremendously grown in recent years.
Defending against Backdoors in Federated Learning with Robust Learning Rate
In addition, we also provide convergence rate analysis for our proposed scheme.
Does Explainable Artificial Intelligence Improve Human Decision-Making?
Explainable AI provides insight into the "why" for model predictions, offering potential for users to better understand and trust a model, and to recognize and correct AI predictions that are incorrect.
Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph
Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world.
ChainNet: Learning on Blockchain Graphs with Topological Features
With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction.
BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain
To our knowledge, none of the previous approaches have employed advanced data analytics techniques to automatically detect ransomware related transactions and malicious Bitcoin addresses.
Topological Data Analysis
Cryptography and Security
Distributed, Parallel, and Cluster Computing
Breaking Transferability of Adversarial Samples with Randomness
We demonstrate that introducing randomness to the DNN models is sufficient to defeat adversarial attacks, given that the adversary does not have an unlimited attack budget.
Adversarial Clustering: A Grid Based Clustering Algorithm Against Active Adversaries
Most of the previous work focused on adversarial classification techniques, which assumed the existence of a reasonably large amount of carefully labeled data instances.
Blockchain: A Graph Primer
Our goal is to provide a concise but complete description of blockchain technology that is accessible to readers with no prior expertise in the field.
Computers and Society
