Computer Sciences Commons^™

Efficient And Secure Federated Learning Against Backdoor Attacks, Yinbin Miao, Rongpeng Xie, Xinghua Li, Zhiquan Liu, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Due to the powerful representation ability and superior performance of Deep Neural Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from both academic and industrial fields. However, its transmitted plaintext data causes privacy disclosure. FL based on Local Differential Privacy (LDP) solutions can provide privacy protection to a certain extent, but these solutions still cannot achieve adaptive perturbation in DNN model. In addition, this kind of schemes cause high communication overheads due to the curse of dimensionality of DNN, and are naturally vulnerable to backdoor attacks due to the inherent distributed characteristic. To solve these issues, …

Integrating Authentication Schemes In Augmented And Virtual Reality Classrooms, Naheem Noah

Electronic Theses and Dissertations

Augmented Reality and Virtual Reality (AR/VR) technologies are revolutionizing educational experiences, but their widespread adoption hinges on addressing critical security and usability challenges, particularly in the domain of user authentication. This research presents an investigation into the security landscape of AR/VR and explores a graphical authentication scheme called “Things” that enhances both security and usability in immersive learning environments. Through a systematic evaluation of popular AR/VR devices and applications, potential vulnerabilities and limitations were identified, such as high usage of pin/passwords which are susceptible to shoulder-surfing attacks, lack of multi-factor authentication, and unclear data-sharing practices. A review of existing knowledge-based …

Ethical Challenges And Solutions Of Generative Ai: An Interdisciplinary Perspective, Mousa Al-Kfairy, Dheya Mostafa, Nir Kshetri, Mazen Insiew, Omar Alfandi

All Works

This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing …

Robust Asynchronous Federated Learning With Time-Weighted And Stale Model Aggregation, Yinbin Miao, Ziteng Liu, Xinghua Li, Meng Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated Learning (FL) ensures collaborative learning among multiple clients while maintaining data locally. However, the traditional synchronous FL solutions have lower accuracy and require more communication time in scenarios where most devices drop out during learning. Therefore, we propose an Asynchronous Federated Learning (AsyFL) scheme using time-weighted and stale model aggregation, which effectively solves the problem of poor model performance due to the heterogeneity of devices. Then, we integrate Symmetric Homomorphic Encryption (SHE) into AsyFL to propose Asynchronous Privacy-Preserving Federated Learning (Asy-PPFL), which protects the privacy of clients and achieves lightweight computing. Privacy analysis shows that Asy-PPFL is indistinguishable under …

Predicting Personality Or Prejudice? Facial Inference In The Age Of Artificial Intelligence, Shilpa Madan, Gayoung Park

Research Collection Lee Kong Chian School Of Business

Facial inference, a cornerstone of person perception, has traditionally been studied through human judgments about personality traits and abilities based on people's faces. Recent advances in artificial intelligence (AI) have introduced new dimensions to this field, employing machine learning algorithms to reveal people's character, capabilities, and social outcomes based just on their faces. This review examines recent research on human and AI-based facial inference across psychology, business, computer science, legal, and policy studies to highlight the need for scientific consensus on whether or not people's faces can reveal their inner traits, and urges researchers to address the critical concerns …

Anopay: Anonymous Payment For Vehicle Parking With Updatable Credential, Yang Yang, Wenyi Xue, Yonghua Zhan, Minming Huang, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

Many existing anonymous parking payment schemes lack high efficiency and flexibility. For instance, the calculation and communication costs involved in payment may linearly increase with the payment amount. In this paper, we propose an anonymous payment system (dubbed AnoPay) for vehicle parking, which leverages updatable attribute-based anonymous credentials and efficient zero-knowledge proof (ZKP) to achieve user anonymity and constant overhead for parking fee payment. To further improve the efficiency, we design a secure parking fee aggregation protocol based on linear homomorphic encryption to aggregate parking transactions, where the amount of each parking transaction is hidden and the privacy of the …

Leveraging Blockchain For Trust Enhancement In Decentralized Marketplaces: A Reputation System Perspective, Meshari Mohammd Aljohani

Computer Science Theses & Dissertations

Centralized marketplaces provide reliable reputation services through a central authority, but this raises concerns about single points of failure, user privacy, and data security. Decentralized marketplaces have emerged to address these issues by enhancing user privacy and transparency and eliminating single points of failure. However, decentralized marketplaces face the challenge of maintaining user trust without a centralized authority. Current blockchain-based marketplaces rely on subjective buyer feedback. Additionally, the transparency in these systems can deter honest reviews due to fear of seller retaliation. To address these issues, we propose a trust and reputation system using blockchain and smart contracts. Our system …

Contextualizing Interpersonal Data Sharing In Smart Homes, Weijia He, Nathan Reitinger, Atheer Almogbil, Yi-Shyuan Chiang, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

A key feature of smart home devices is monitoring the environment and recording data. These devices provide security via motion-detection video alerts, cost-savings via thermostat usage history, and peace of mind via functions like auto-locking doors or water leak detectors. At the same time, the sharing of this information in interpersonal relationships---though necessary---is currently accomplished on an all-or-nothing basis. This can easily lead to oversharing in a multi-user environment. Although prior work has studied people's perceptions of information sharing with vendors or ISPs, the sharing of household data among users who interact personally is less well understood. Interpersonal situations make …

A Framework For Evaluating The Security And Privacy Of Smart-Home Devices, And Its Application To Common Platforms, Ravindra Mangar, Timothy Pierson, David Kotz

Dartmouth Scholarship

In this article, we outline the challenges associated with the widespread adoption of smart devices in homes. These challenges are primarily driven by scale and device heterogeneity: a home may soon include dozens or hundreds of devices, across many device types, and may include multiple residents and other stakeholders. We develop a framework for reasoning about these challenges based on the deployment, operation, and decommissioning life cycle stages of smart devices within a smart home. We evaluate the challenges in each stage using the well-known CIA triad—Confidentiality, Integrity, and Availability. In addition, we highlight open research questions at each stage. …

Enhancing Security In Modern Medical Devices: The Medicalharm Methodology And Cyberllama2, Emmanuel Kwarteng

Dissertations (1934 -)

With the rapid growth of Modern Medical Devices (MMDs) and their increasing connectivity to enhance patient care, concerns about security, privacy, and safety are paramount. If compromised, these devices can expose sensitive patient information and harm patients. Therefore, securing MMDs against cyber-attacks is critical. Threat modeling, mandated by the FDA as a premarket submission requirement in the MMD domain, serves as the first defense mechanism. However, our investigation of 119 participants from various MMD manufacturing companies revealed a need for a tailored threat modeling methodology that considers both patient safety and device complexity. To address this, we present MEDICALHARM, a …

Student Partners In Ai Literacy: A Library And Writing Center Collaboration, A. P. Anderson

Velma K. Waters Library Faculty Publications

Student voices are valuable but often overlooked in discussions surrounding the role of AI in higher education. AI Literacy education efforts that treat students only as a potential audience for instruction rather than as potential instructors themselves miss out on the passion, curiosity, and complex questions that students can bring to these conversations. If we center student voices in AI Literacy education discussions, and encourage both their enthusiasm and skepticism, students can become comfortable and confident in leading discussions about AI in the classroom and in their lives. In my proposed poster presentation, I will share insights from an AI …

Trust, Transparency, And Transport: The Impact Of Privacy Protection On The Acceptance Of Last-Mile Drone Delivery, Jurgen Heinz Famula

Electronic Theses and Dissertations

A common set of problems commercial delivery companies face is finding ways to increase the efficiency and reliability of the “last mile” of a package’s journey, all while reducing operating costs. This need for efficiency has driven many companies to explore using unmanned aerial vehicles (UAVs), or drones, to get packages to their final destination. Although UAVs have great potential to help increase efficiency in commercial package delivery, this comes at a potential cost to the privacy of people who intersect the flight paths of these unmanned vehicles. This thesis explores the effect of a mobile phone application for commercial …

More Human-Likeness, Less Self-Disclosure? Avatars' Form Realism And Job Applicants' Self-Disclosure In Ai Interviews, Yamin Xu, Keng Siau, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

The rise of AI in recruitment promises to revolutionize how organizations evaluate job candidates. The quality of AI evaluations is determined by the input data, which depends on job applicants' self-disclosure. However, little is known about how the design elements of AI interview systems, particularly avatar interviewers, influence job applicants' self-disclosure during these interactions. This study aims to address this gap by specifically focusing on how the form realism of avatar interviewers affects job applicants' self-disclosure through their perceptions. In addition, the study will examine the effects of job type as a moderator. Drawing on the Stimulus-Organism-Response (S-O-R) model, this …

Measuring Confidentiality With Multiple Observables, John J. Utley

Computer Science Senior Theses

Measuring the confidentiality of programs that need to interact with the outside world can prevent leakages and is important to protect against dangerous attacks. However, information propagation is difficult to follow through a large program with implicit information flow, tricky loops, and complicated instructions. Previous works have tackled this problem in several ways but often measure leakage a program has on average rather than the leakage produced by a set of particularly compromising interactions. We introduce new methods that target a specific set of observables revealed throughout execution to cut down on the resources needed for analysis. Our implementation examines …

Whisper: Proximity-Based Authentication For Securely Sharing Secrets, Charles A. Vogel

Computer Science Senior Theses

Cryptography offers a wide arsenal of encryption methods to enable secure communication between two
devices that have already exchanged a shared secret. Existing techniques for exchanging a shared secret,
however, are often vulnerable to man-in-the-middle attacks, require special hardware for out-of-band com-
munications, or require a pre-existing Internet connection. With the constantly increasing prevalence of
Internet of Things (IoT) devices sharing sensitive information via wireless networks, the need for a method
to establish secure communication is more pressing than ever.
With this context, we present Whisper, a novel technique that combines elements of digital commu-
nication theory, cryptography, and probability …

We Need A “Building Inspector For Iot” When Smart Homes Are Sold, Timothy J. Pierson, Cesar Arguello, Beatrice Perez, Wondimu Zegeye, Kevin Kornegay, Carl A. Gunter

Dartmouth Scholarship

Internet of Things (IoT) devices left behind when a home is sold create security and privacy concerns for both prior and new residents. We envision a specialized “building inspector for IoT” to help securely facilitate transfer of the home.

Attribute-Hiding Fuzzy Encryption For Privacy-Preserving Data Evaluation, Zhenhua Chen, Luqi Huang, Guomin Yang, Willy Susilo, Xingbing Fu, Xingxing Jia

Research Collection School Of Computing and Information Systems

Privacy-preserving data evaluation is one of the prominent research topics in the big data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this paper, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap …

A Roadmap For Applying The Contextual Integrity Framework In Qualitative Privacy Research, Priya C. Kumar, Michael Zimmer, Jessica Vitak

Computer Science Faculty Research and Publications

Privacy is an important topic in HCI and social computing research, and the theory of contextual integrity (CI) is increasingly used to understand how sociotechnical systems-and the new kinds of information flows they introduce-can violate privacy. In empirical research, CI can serve as a conceptual framework for explaining the contextual nature of privacy as well as an analytical framework for evaluating privacy attitudes and behaviors. Analytical applications of CI in HCI primarily employ quantitative methods to identify appropriate information flows but rarely engage with the full CI framework to evaluate such flows. In this paper, we present a roadmap to …

Book Review: Tracers In The Dark: The Global Hunt For The Crime Lords Of Cryptocurrency, Marion Jones

International Journal of Cybersecurity Intelligence & Cybercrime

Doubleday released Andy Greenberg’s Tracers in the Dark: The Global Hunt for the Crime Lords of Cryptocurrency in November 2022. Through vivid case studies of global criminal investigations, the book dispels myths about the anonymizing power of cryptocurrency. The book details how the ability to identify cryptocurrency users and payment methods successfully brought down several large criminal empires, while also highlighting the continuous cat-and-mouse game between law enforcement officials and criminal actors using cryptocurrency. The book is an excellent resource for law enforcement officials, academics, and general cybersecurity practitioners interested in cryptocurrency-related criminal activities and law enforcement techniques.

Fast And Reliable Authentication Method For Indoor Constrained Drones, Fatima Ali Alnuaimi

Thesis/ Dissertation Defenses

Over the last twenty years, the commercial sector of Unmanned Aerial Vehicles has been growing exponentially, owing to their rapid deployment, high mobility, and the number of applications in the industry field such as military, transportation, critical infrastructures, as well as in the academic field for research purposes. One of the main communication systems adopted by the UAVs relies on transmitting wireless signals. In particular, UAVs are adopted for indoor use-case scenarios, such as warehouse inventory applications and indoor building inspections. They need to transmit control messages and sensitive data by leveraging an efficient, short-range, and secure communication channel to …

Fast And Reliable Authentication Method For Indoor Constrained Drones, Fatima Ali Al Nuaimi

Theses

Over the last twenty years, the commercial sector of Unmanned Aerial Vehicles has been growing exponentially, owing to their rapid deployment, high mobility, and the number of applications in the industry field such as military, transportation, critical infrastructures, as well as in the academic field for research purposes. One of the main communication systems adopted by the UAVs relies on transmitting wireless signals.

In particular, UAVs are adopted for indoor use-case scenarios, such as warehouse inventory applications and indoor building inspections. They need to transmit control messages and sensitive data by leveraging an efficient, short-range, and secure communication channel to …

Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das

Computer Science Faculty Research & Creative Works

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to …

Data Driven Trade-Off Analysis For Cybersecurity, Goskel Kucukkaya, Murat Ozer, Murat Balci, Emrah Ugurlu

Engineering Management & Systems Engineering Faculty Publications

Trade-off analysis, a specialization of systems engineering, addresses design criteria like security, cost, performance, and compliance. Monte Carlo simulations are commonly employed to generate impact scenarios for trade-off analysis combined with solution alternatives that accommodate industry-specific considerations and uncertainties. In the cyber domain, this paper proposes a methodology for data-driven trade-off analysis in cybersecurity, leveraging industry reports as primary data sources using confidentiality, integrity, and availability as trade-off analysis objectives. Distribution functions are derived to manage and model uncertainties for various industries. The approach given in this study aims to facilitate informed choices and to enhance cybersecurity decision making and …

Privacy Vs. Social Capital: Examining Information Disclosure Patterns Within Social Media Influencer Networks, Eidan James Rosado

CCAC Theses and Dissertations

Adversaries have several ways to leverage and expose the data disclosed in social media network engagements for different motives including but not limited to fraud, discreditation, or social engineering. Previous research on social media interactions discussed increased engagements where influencers and viral trends were involved. Studies also discussed engagements declining over time. Within posts or engagements, personal identifiable information (PII) can be shared with varying rate of risk severity. Publicly available data such as this can be leveraged by adversaries. With the absence of insights of whether influencers impact engagements and disclosures, the goal of this study was to obtain …

Attitudes And Perceptions Towards Privacy And Surveillance In Australia, Aleatha J. Shanley

Theses: Doctorates and Masters

Understanding attitudes towards privacy and surveillance technologies used to enhance security objectives is a complex, but crucial aspect for policy makers to consider. Historically, terrorism-related incidents justified the uptake of surveillance practices. More recently however, biosecurity concerns have motivated nation-states to adopt more intrusive surveillance measures. There is a growing body of literature that supports the public’s desire to maintain privacy despite fears of biological or physical threats.

This research set out to explore attitudes towards privacy and surveillance in an Australian context. Throughout the course of this endeavour, the COVID-19 pandemic emerged bringing with it a variety of track …

An Extensive Examination Of The Iot And Blockchain Technologies In Relation To Their Applications In The Healthcare Industry, Karthik Kumar Vaigandla, Madhu Kumar Vanteru, Mounika Siluveru

Mesopotamian Journal of Computer Science

Numerous domains have been transformed by the communication technologies made possible by the Internet of Things (IoT), one of which is health monitoring systems. Patterns associated with diseases and health conditions can be identified through the utilization of machine learning and cutting-edge AI techniques. Currently, scientific endeavours are concentrated on enhancing IoT-enabled applications such as medical report administration, prescription traceability, and infectious disease surveillance through the amalgamation of blockchain technology(BCT) and machine learning(ML) models. Although recent advancements have attempted to increase the adaptability of blockchain(BC) and ML for IoT applications, there are still a number of crucial considerations that must …

Designing A Blockchain-Empowered Telehealth Artifact For Decentralized Identity Management And Trustworthy Communication: Interdisciplinary Approach, Xueping Liang, Nabid Alam, Tahmina Sultana, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks.

Objective: The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance.

Methods: We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype …

Blockchain For Computational Integrity And Privacy, Rahul Raj

Theses and Dissertations

This study proposes a blockchain based system that utilizes fully homomorphic encryption to provide security of data in use as well as computational integrity. This is achieved by leveraging the attributes of blockchain which provides availability and data integrity combined with homomorphic encryption that provides confidentiality. The proposed system is designed to perform statistical operations, including mean, median and variance, on encrypted data, thus providing confidentiality of data while in use. The computations are performed on the smart contract, residing on the blockchain which provides computational integrity. The results indicate that it is possible to perform fully homomorphic computations on …

A Systematic Review Of K-12 Cybersecurity Education Around The World, Ahmed Ibrahim, Marnie Mckee, Leslie F. Sikos, Nicola F. Johnson

Research outputs 2022 to 2026

This paper presents a systematic review of K-12 cybersecurity education literature from around the world. 24 academic papers dated from 2013-2023 were eligible for inclusion in the literature established within the research protocol. An additional 19 gray literature sources comprised the total. A range of recurring common topics deemed as aspects of cybersecurity behavior or practice were identified. A variety of cybersecurity competencies and skills are needed for K-12 students to apply their knowledge. As may be expected to be the case with interdisciplinary fields, studies are inherently unclear in the use of their terminology, and this is compounded in …

Synthetic Health Data: Real Ethical Promise And Peril, W. Nicholson Price Ii, Daniel Susser

Other Publications

Modern health research and development faces a dilemma. On the one hand, there is more data than ever — in electronic health records, in lab research, in public datasets, and on the internet — from which to extract potentially transformative scientific insights and to use as the basis for developing breakthrough health care technologies. On the other hand, using this data entails various risks: threats to patient privacy, skewed samples and approaches to analysis that can perpetuate demographic and other biases, and uneven access to data about rare conditions and small patient subgroups. Generating synthetic data has emerged as one …