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Full-Text Articles in Physical Sciences and Mathematics
Divide-And-Conquer Distributed Learning: Privacy-Preserving Offloading Of Neural Network Computations, Lewis C.L. Brown
Divide-And-Conquer Distributed Learning: Privacy-Preserving Offloading Of Neural Network Computations, Lewis C.L. Brown
Graduate Theses and Dissertations
Machine learning has become a highly utilized technology to perform decision making on high dimensional data. As dataset sizes have become increasingly large so too have the neural networks to learn the complex patterns hidden within. This expansion has continued to the degree that it may be infeasible to train a model from a singular device due to computational or memory limitations of underlying hardware. Purpose built computing clusters for training large models are commonplace while access to networks of heterogeneous devices is still typically more accessible. In addition, with the rise of 5G networks, computation at the edge becoming …
Right To Know, Right To Refuse: Towards Ui Perception-Based Automated Fine-Grained Permission Controls For Android Apps, Vikas Kumar Malviya, Chee Wei Leow, Ashok Kasthuri, Naing Tun Yan, Lwin Khin Shar, Lingxiao Jiang
Right To Know, Right To Refuse: Towards Ui Perception-Based Automated Fine-Grained Permission Controls For Android Apps, Vikas Kumar Malviya, Chee Wei Leow, Ashok Kasthuri, Naing Tun Yan, Lwin Khin Shar, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
It is the basic right of a user to know how the permissions are used within the Android app’s scope and to refuse the app if granted permissions are used for the activities other than specified use which can amount to malicious behavior. This paper proposes an approach and a vision to automatically model the permissions necessary for Android apps from users’ perspective and enable fine-grained permission controls by users, thus facilitating users in making more well-informed and flexible permission decisions for different app functionalities, which in turn improve the security and data privacy of the App and enforce apps …
Dataset Evaluation For Data Trading Using Expected Loss And Homomorphic Encryption, Minsung Joo
Dataset Evaluation For Data Trading Using Expected Loss And Homomorphic Encryption, Minsung Joo
Senior Honors Papers / Undergraduate Theses
Supervised machine learning suffers from the ``garbage-in garbage-out" phenomenon where the performance of a model is limited by the quality of the data. While a myriad of data is collected every second, there is no general rigorous method of evaluating the quality of a given dataset. This hinders fair pricing of data in scenarios where a buyer may look to buy data for use with machine learning. In this work, I propose using the expected loss corresponding to a dataset as a measure of its quality, relying on Bayesian methods for uncertainty quantification. Furthermore, I present a secure multi-party computation …
Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Aoyu Wu, Huan Wei, Huamin. Qu
Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Aoyu Wu, Huan Wei, Huamin. Qu
Research Collection School Of Computing and Information Systems
With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. …
Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu
Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu
Modeling, Simulation and Visualization Student Capstone Conference
The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, …
Automated Reverse Engineering Of Role-Based Access Control Policies Of Web Applications, Ha Thanh Le, Lwin Khin Shar, Domenico Bianculli, Lionel C. Briand, Cu Duy Nguyen
Automated Reverse Engineering Of Role-Based Access Control Policies Of Web Applications, Ha Thanh Le, Lwin Khin Shar, Domenico Bianculli, Lionel C. Briand, Cu Duy Nguyen
Research Collection School Of Computing and Information Systems
Access control (AC) is an important security mechanism used in software systems to restrict access to sensitive resources. Therefore, it is essential to validate the correctness of AC implementations with respect to policy specifications or intended access rights. However, in practice, AC policy specifications are often missing or poorly documented; in some cases, AC policies are hard-coded in business logic implementations. This leads to difficulties in validating the correctness of policy implementations and detecting AC defects.In this paper, we present a semi-automated framework for reverse-engineering of AC policies from Web applications. Our goal is to learn and recover role-based access …
A Predictive Model To Predict Cyberattack Using Self-Normalizing Neural Networks, Oluwapelumi Eniodunmo
A Predictive Model To Predict Cyberattack Using Self-Normalizing Neural Networks, Oluwapelumi Eniodunmo
Theses, Dissertations and Capstones
Cyberattack is a never-ending war that has greatly threatened secured information systems. The development of automated and intelligent systems provides more computing power to hackers to steal information, destroy data or system resources, and has raised global security issues. Statistical and Data mining tools have received continuous research and improvements. These tools have been adopted to create sophisticated intrusion detection systems that help information systems mitigate and defend against cyberattacks. However, the advancement in technology and accessibility of information makes more identifiable elements that can be used to gain unauthorized access to systems and resources. Data mining and classification tools …
Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos
Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos
VMASC Publications
Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …
Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu
Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu
Computer Science Faculty Publications
Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in …
Faking Sensor Noise Information, Justin Chang
Faking Sensor Noise Information, Justin Chang
Master's Projects
Noise residue detection in digital images has recently been used as a method to classify images based on source camera model type. The meteoric rise in the popularity of using Neural Network models has also been used in conjunction with the concept of noise residuals to classify source camera models. However, many papers gloss over the details on the methods of obtaining noise residuals and instead rely on the self- learning aspect of deep neural networks to implicitly discover this themselves. For this project I propose a method of obtaining noise residuals (“noiseprints”) and denoising an image, as well as …