Noman Mohammed
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View article: Design of a Secure Biometric Network Traffic Packet Data Transmission using Deep Learning and Encryption Techniques
Design of a Secure Biometric Network Traffic Packet Data Transmission using Deep Learning and Encryption Techniques Open
This study presents a secure and efficient system for processing and transmitting biometric network traffic packet data by integrating Convolutional Neural Networks (CNN) and Paillier Homomorphic Encryption (PHE) techniques. In the system,…
View article: Enhancing Risk Management with Human Factors in Cybersecurity Using Behavioural Analysis and Machine Learning Technique
Enhancing Risk Management with Human Factors in Cybersecurity Using Behavioural Analysis and Machine Learning Technique Open
This study presents the development of an intelligent cybersecurity risk management system that leverages behavioural analytics and machine learning to detect threats and anomalous user activities in real time. The system was developed in …
View article: Robust privacy amidst innovation with large language models through a critical assessment of the risks
Robust privacy amidst innovation with large language models through a critical assessment of the risks Open
Objective This study evaluates the integration of electronic health records (EHRs) and natural language processing (NLP) with large language models (LLMs) to enhance healthcare data management and patient care, focusing on using advanced l…
View article: Privacy-Preserving Fair Synthetic Tabular Data
Privacy-Preserving Fair Synthetic Tabular Data Open
Sharing of tabular data containing valuable but private information is limited due to legal and ethical issues. Synthetic data could be an alternative solution to this sharing problem, as it is artificially generated by machine learning al…
View article: Application of artificial neural networks (ANN) to evaluate centrifugal pump characteristics
Application of artificial neural networks (ANN) to evaluate centrifugal pump characteristics Open
This paper uses an Artificial Neural Network (ANN) technique to give an experimental and comparative examination of centrifugal pump characteristics. Comprehensive physical testing is a common component of traditional pump performance eval…
View article: Not Fully Synthetic: LLM-based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing.
Not Fully Synthetic: LLM-based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing. Open
The publication and sharing of clinical notes are crucial for healthcare research and innovation. However, privacy regulations such as HIPAA and GDPR pose significant challenges. While de-identification techniques aim to remove protected h…
View article: Synthetic Data: Revisiting the Privacy-Utility Trade-off
Synthetic Data: Revisiting the Privacy-Utility Trade-off Open
Synthetic data has been considered a better privacy-preserving alternative to traditionally sanitized data across various applications. However, a recent article challenges this notion, stating that synthetic data does not provide a better…
View article: De-identification is not always enough
De-identification is not always enough Open
For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data …
View article: Secure Genomic String Search with Parallel Homomorphic Encryption
Secure Genomic String Search with Parallel Homomorphic Encryption Open
Fully homomorphic encryption (FHE) cryptographic systems enable limitless computations over encrypted data, providing solutions to many of today’s data security problems. While effective FHE platforms can address modern data security conce…
View article: Privacy preserving vertical distributed learning for health data
Privacy preserving vertical distributed learning for health data Open
Federated learning has become a pivotal tool in healthcare, enabling valuable insights to be gleaned from disparate datasets held by cautious data owners concerned about data privacy. This method involves the analysis of data from diverse …
View article: Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning
Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning Open
The vulnerability of machine learning models to membership inference attacks, which aim to determine whether a specific record belongs to the training dataset, is explored in this paper. Federated learning allows multiple parties to indepe…
View article: Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources Open
Objectives: This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. …
View article: Differential Private Deep Learning Models for Analyzing Breast Cancer Omics Data
Differential Private Deep Learning Models for Analyzing Breast Cancer Omics Data Open
Proper analysis of high-dimensional human genomic data is necessary to increase human knowledge about fundamental biological questions such as disease associations and drug sensitivity. However, such data contain sensitive private informat…
View article: De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective Open
In this work, we propose a novel problem formulation for de-identification of\nunstructured clinical text. We formulate the de-identification problem as a\nsequence to sequence learning problem instead of a token classification\nproblem. O…
View article: Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources Open
Objectives: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM), and compares the developed model's outcomes with each other, as well as that from the standard R package (`lme4'). Methods: …
View article: Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification
Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification Open
Among the many music information retrieval (MIR) tasks, music genre classification is noteworthy. The categorization of music into different groups that came to existence through a complex interplay of cultures, musicians, and various mark…
View article: Privacy-preserving string search on encrypted genomic data using a generalized suffix tree
Privacy-preserving string search on encrypted genomic data using a generalized suffix tree Open
Background and objective: Efficient sequencing technologies generate a plethora of genomic data and make it available to researchers. To compute a massive genomic dataset, outsourcing the data to the cloud is often required. Before outsour…
View article: CPU and GPU Accelerated Fully Homomorphic Encryption
CPU and GPU Accelerated Fully Homomorphic Encryption Open
Fully Homomorphic Encryption (FHE) is one of the most promising technologies for privacy protection as it allows an arbitrary number of function computations over encrypted data. However, the computational cost of these FHE systems limits …
View article: PAARS: Privacy Aware Access Regulation System
PAARS: Privacy Aware Access Regulation System Open
During pandemics, health officials usually recommend access monitoring and regulation protocols/systems in places that are major activity centres. As organizations adhere to those recommendations, they often fail to implement proper privac…
View article: A Deep Learning Framework for Malware Classification
A Deep Learning Framework for Malware Classification Open
In this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial institutions, businesses, …
View article: Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation
Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation Open
To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framewo…
View article: Privacy-preserving techniques of genomic data—a survey
Privacy-preserving techniques of genomic data—a survey Open
Genomic data hold salient information about the characteristics of a living organism. Throughout the past decade, pinnacle developments have given us more accurate and inexpensive methods to retrieve genome sequences of humans. However, wi…