Olivera Kotevska
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View article: OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC Open
View article: HEMERA: A Human-Explainable Transformer Model for Estimating Lung Cancer Risk using GWAS Data
HEMERA: A Human-Explainable Transformer Model for Estimating Lung Cancer Risk using GWAS Data Open
Lung cancer (LC) is the third most common cancer and the leading cause of cancer deaths in the US. Although smoking is the primary risk factor, the occurrence of LC in never-smokers and familial aggregation studies highlight a genetic comp…
View article: Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy Open
Federated Learning with client-level differential privacy (DP) provides a promising framework for collaboratively training models while rigorously protecting clients' privacy. However, classic approaches like DP-FedAvg struggle when client…
View article: Privacy by Design in Distributed Edge Systems: Innovating Secure Workflows for Smart Cities
Privacy by Design in Distributed Edge Systems: Innovating Secure Workflows for Smart Cities Open
The proliferation of distributed edge systems, such as those in smart cities, healthcare, and industrial IoT, offers unprecedented opportunities for data processing closer to its source, thereby reducing latency and enhancing efficiency. H…
View article: Privacy-Preserving Federated Learning for Science: Challenges and Research Directions
Privacy-Preserving Federated Learning for Science: Challenges and Research Directions Open
This paper discusses the key challenges and future research directions for privacy-preserving federated learning (PPFL), with a focus on its application to large-scale scientific AI models, in particular, foundation models~(FMs). PPFL enab…
View article: A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications Open
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as…
View article: Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations Open
Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, t…
View article: Frequency Oracle for Sensitive Data Monitoring
Frequency Oracle for Sensitive Data Monitoring Open
As data privacy issues grow, finding the best privacy preservation algorithm for each situation is increasingly essential. This research has focused on understanding the frequency oracles (FO) privacy preservation algorithms. FO conduct th…
View article: Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations Open
Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, t…
View article: LUCID Thrust 1 - Dataset Identification and Biodata Catalog Creation
LUCID Thrust 1 - Dataset Identification and Biodata Catalog Creation Open
The LUCID DOE consortium, part of the Department of Energy’s Biological and Environmental Research (BER) program, advances Low Dose Radiation (LDR) research through multidisciplinary efforts across seven key thrusts. This document focuses …
View article: Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins
Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins Open
Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and pr…
View article: Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy Open
Federated Learning (FL) has emerged as a leading paradigm for decentralized, privacy preserving machine learning training. However, recent research on gradient inversion attacks (GIAs) have shown that gradient updates in FL can leak inform…
View article: Frequency Oracle for Sensitive Data Monitoring (Student Abstract)
Frequency Oracle for Sensitive Data Monitoring (Student Abstract) Open
As data privacy issues grow, finding the best privacy preservation algorithm for each situation is increasingly essential. This research has focused on understanding the frequency oracles (FO) privacy preservation algorithms. FO conduct th…
View article: Privacy policy robustness to reverse engineering
Privacy policy robustness to reverse engineering Open
Differential privacy policies allow one to preserve data privacy while sharing and analyzing data. However, these policies are susceptible to an array of attacks. In particular, often a portion of the data desired to be privacy protected i…
View article: RL-HEMS: Reinforcement Learning Based Home Energy Management System for HVAC Energy Optimization
RL-HEMS: Reinforcement Learning Based Home Energy Management System for HVAC Energy Optimization Open
Heating, ventilation, and air conditioning (HVAC) is one of the major energy consumers in the residential sector. It is important to be able to monitor and control the energy consumed to provide utility services such as load shaping while …
View article: Privacy Amplification for Episodic Training Methods
Privacy Amplification for Episodic Training Methods Open
It has been shown that differential privacy bounds improve when subsampling within a randomized mechanism. Episodic training, utilized in many standard machine learning techniques, uses a multistage subsampling procedure which has not been…
View article: Whitepaper on Reusable Hybrid and Multi-Cloud Analytics Service Framework
Whitepaper on Reusable Hybrid and Multi-Cloud Analytics Service Framework Open
Over the last several years, the computation landscape for conducting data analytics has completely changed. While in the past, a lot of the activities have been undertaken in isolation by companies, and research institutions, today's infr…
View article: A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications Open
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as…
View article: Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments
Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments Open
View article: ECICE 2022 Cover Page
ECICE 2022 Cover Page Open
View article: Investigating Users’ Privacy Concerns of Internet of Things (IoT) Smart Devices
Investigating Users’ Privacy Concerns of Internet of Things (IoT) Smart Devices Open
Although the number of smart Internet of Things (IoT) devices has grown in recent years, the public's perception of how effectively these devices secure IoT data has been questioned. Many IoT users do not have a good level of confidence in…
View article: PAS: Privacy Algorithms in Systems
PAS: Privacy Algorithms in Systems Open
Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems shar…
View article: A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS
A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS Open
Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we…
View article: Analyzing Data Privacy for Edge Systems
Analyzing Data Privacy for Edge Systems Open
Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency bene…
View article: Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing
Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing Open
View article: Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data
Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data Open
Various Internet of Things (IoT) devices generate complex, dynamically changed, and infinite data streams. Adversaries can cause harm if they can access the user's sensitive raw streaming data. For this reason, protecting the privacy of th…
View article: Effectiveness of Privacy Techniques in Smart Metering Systems
Effectiveness of Privacy Techniques in Smart Metering Systems Open
Smart grid technologies enable timely energy billing for residential homes. The ability to react to energy demands during peak hours allows energy providers to conserve power and operate efficiently. However, these data streams are also su…
View article: Optimal Balance of Privacy and Utility with Differential Privacy Deep Learning Frameworks
Optimal Balance of Privacy and Utility with Differential Privacy Deep Learning Frameworks Open
As the number of online services has increased, the amount of sensitive data being recorded is rising. Simultaneously, the decision-making process has improved by using the vast amounts of data, where machine learning has transformed entir…
View article: Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics
Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics Open
The 2020 Smoky Mountains Computational Sciences and Engineering Conference enlists research scientists from across Oak Ridge National Laboratory (ORNL) to be data sponsors and help create data analytics challenges for eminent data sets at …
View article: Challenges in Automated Detection of COVID-19 Misinformation
Challenges in Automated Detection of COVID-19 Misinformation Open
The COVID-19 pandemic has made the dangers of the spread of misinformation obvious but despite much global effort to curbing its spread, fake information about the pandemic keeps proliferating. In this paper, we address the development of …