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DEXO: A Secure and Fair Exchange Mechanism for Decentralized IoT Data Markets

IEEE Internet of Things Journal 2025年第11期12卷 16095-16111页

作者： Li, Yue Alom, Ifteher Sun, Wenhai Xiao, Yang University of Kentucky Department of Computer Science LexingtonKY United States Purdue University Department of Computer and Information Technology West LafayetteIN United States

Opening up data produced by the Internet of Things (IoT) and mobile devices for public utilization can maximize their economic value. Challenges remain in the trustworthiness of the data sources and the security of the trading process, particularly when there is no trust between the data providers and consumers. In this paper, we propose DEXO, a decentralized data exchange mechanism that facilitates secure and fair data exchange between data consumers and distributed IoT/mobile data providers at scale, allowing the consumer to verify the data generation process and the providers to be compensated for providing authentic data, with correctness guarantees from the exchange platform. To realize this, DEXO extends the decentralized oracle network model that has been successful in the blockchain applications domain to incorporate novel hardware-cryptographic co-design that harmonizes trusted execution environment, secret sharing, and smart contract-assisted fair exchange. For the first time, DEXO ensures end-to-end data confidentiality, source verifiability, and fairness of the exchange process with strong resilience against participant collusion. We implemented a prototype of the DEXO system to demonstrate feasibility. The evaluation shows a moderate deployment cost and significantly improved blockchain operation efficiency compared to a popular data exchange mechanism. © 2025 IEEE.

关键词： Internet of Things Blockchains Smart contracts Soft sensors Security Servers Resilience Decentralized applications Data models Oceans

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Investigating the Industry Perceptions and Use of AI Tools in Project Management: Implications for Educating Future Engineers

Investigating the Industry Perceptions and Use of AI Tools i...

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2024 ASEE Annual Conference and Exposition

作者： Aggrawal, Sakhi Thomas, Paul J. Purdue University United States The Department of Computer and Information Technology Purdue University United States

Background: The rapid evolution of Artificial Intelligence (AI) has opened up numerous avenues for automating various tasks in diverse industries. Project management, a crucial element in engineering education, can greatly benefit from AI tools, such as chatbots and virtual assistants. ChatGPT, a variant of the GPT series known for its conversational capabilities, exemplifies the growing prominence of AI in everyday applications, becoming almost synonymous with accessible AI. As the boundaries of what AI can achieve are continually pushed, engineering education must also evolve alongside these advancements. Through this research, we aim to align the current industry usage of AI in project management with academic approaches. Purpose: The purpose of this study is to understand the current usage and perceptions of industry professionals about AI tools in project management tasks. The specific research questions are: (1) What factors influence the usage of AI tools in project management practices? (2) How are project managers currently using AI tools? (3) What are their perceptions of these tools? Methods: A survey was designed to gauge industry professionals' usage and perceptions regarding AI's tools in project management tasks and included questions to gather demographic data. This survey was shared across multiple project management groups on LinkedIn over a three-month duration, attracting 113 responses. A cleaning process was implemented to remove any invalid responses. A correlational analysis was performed on the quantitative data. Concurrently, qualitative data was thematically analyzed to gain insights into usage and perceptions surrounding AI. Results: The study revealed a growing trend among project management professionals in leveraging AI tools for a variety of tasks, including project planning, task assignment, tracking, and crafting emails, reports, and presentations. A strong correlation was observed between familiarity with ChatGPT and its likely usag

关键词： Decision making

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Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes 31

Hire Me or Not? Examining Language Model's Behavior with Occ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Damin Zhang, Yi Bihani, Geetanjali Rayz, Julia Department of Computer and Information Technology Purdue University West Lafayette United States

ISBN: (纸本)9798891761964

With the impressive performance in various downstream tasks, large language models (LLMs) have been widely integrated into production pipelines, such as recruitment and recommendation systems. A known issue of models trained on natural language data is the presence of human biases, which can impact the fairness of the system. This paper investigates LLMs' behavior with respect to gender stereotypes in the context of occupation decision making. Our framework is designed to investigate and quantify the presence of gender stereotypes in LLMs' behavior via multi-round question answering. Inspired by prior work, we constructed a dataset using a standard occupation classification knowledge base released by authoritative agencies. We tested it on three families of LMs (RoBERTa, GPT, and Llama) and found that all models exhibit gender stereotypes analogous to human biases, but with different preferences. The distinct preferences of GPT-3.5-turbo and Llama2-70b-chat, along with additional analysis indicating GPT-4o-mini favors female subjects, may imply that the current alignment methods are insufficient for debiasing and could introduce new biases contradicting the traditional gender stereotypes. Our contribution includes a 73,500 prompts dataset constructed with a taxonomy of real-world occupations and a multi-step verification framework to evaluate model's behavior regarding gender stereotype. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Shopping While Watching: An Updated Forensic Analysis of TikTok on Android and iOS

Shopping While Watching: An Updated Forensic Analysis of Tik...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Hu, Xiao Karabiyik, Umit Purdue University Computer and Information Technology West Lafayette United States

ISBN: (纸本)9798350364910

TikTok is one of the most popular social media platforms in the world. Despite its extensive use, mobile forensics research on TikTok has seen little progress in the past two years, possibly due to limitations faced by some researchers using this app. This paper presents a forensic analysis of TikTok, focusing on recent inadequacies in mobile forensic research on it, especially with the advent of features like the TikTok Shop. This study not only describes the forensic value of the new shopping feature, but also updates artifacts from previous studies on general information and media sections, providing investigators with information about TikTok-related folder structures and important data paths. For general information, we extracted data about user accounts and apps, and for media information, we found video content uploaded and viewed by users, and proposed possible methods to distinguish what users actually watched. This work improves the understanding of the digital environment and offers valuable forensic insights crucial to keeping up with rapid technological advancements in social media platforms for digital forensic investigations. © 2024 IEEE.

关键词： Android Data Forensics iOS Path TikTok

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Forensics Analysis of Android Social Networking Application: Tencent QQ Revisited

Forensics Analysis of Android Social Networking Application:...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Gong, Yufeng Karabiyik, Umit Computer and Information Technology Purdue University West Lafayette United States

ISBN: (纸本)9798350364910

Tencent QQ, founded in 1999, is a widely used instant messaging application globally. At its peak, it had 900 million users. Although numerous similar applications have emerged, such as WeChat, QQ continues to maintain its competitiveness in business and interest-based communities, particularly among young adults. There have been many forensic studies on QQ since 2009, focusing mainly on its memory function, instant messaging function, and computer version. However, many useful functions of mobile QQ have not been thoroughly researched. Additionally, online fraudsters, particularly scam syndicates in Southeast Asia, have exploited these opportunities. The application allows scammers to contact victims and extort property. To address this gap, this research conducted a forensic analysis of QQ's new functions on Android devices. It focused on new features including device detection, file editing and transferring, the built-in camera, shared documents, payment and service functions, message withdrawal, real-time location sharing, phone numbers, contacts, QQ group activities, chat history and its backup, QQ zone, payment, and private information preservation and protection. This research aims to assist investigators in effectively utilizing forensic techniques for QQ and its new features, to understand what evidence can be obtained and what can be recovered, thereby addressing the existing knowledge gap. © 2024 IEEE.

关键词： Instant messaging applications

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Identifying Media Storage Locations in Baby Camera Apps: A Mobile Forensic Approach

Identifying Media Storage Locations in Baby Camera Apps: A M...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Lima, Vinicius Karabiyik, Umit Purdue University Computer and Information Technology West Lafayete United States

ISBN: (纸本)9798350364910

With the proliferation of IoT and its integration with the internet, digital forensics has encountered a diverse array of potentially questioned devices. Baby cameras represent an example of such devices, characterized by a dearth of background information on forensic analysis and content storage mechanisms within devices and their associated applications. Given the potential for these devices to capture criminal behavior, notably instances of child abuse, there exists a compelling need to comprehensively understand the storage methodologies employed, particularly concerning media content. This study aims to bridge this gap by conducting forensic analyses on two popular baby cameras, with a focus on identifying potential data sources pertinent to investigators. Our investigation prioritized photos and video content, leading to the identification of seven distinct locations where media may be stored. These locations were meticulously detailed, with a particular emphasis on determining those capable of retaining deliberately deleted media. Despite our focus on media, we scrutinized the applications' log files and databases, uncovering potentially valuable information relevant to investigations, including details on Wi-Fi connections and user interactions. © 2024 IEEE.

关键词： Electronic crime countermeasures

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A trustless architecture of blockchain-enabled metaverse

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High-Confidence Computing 2023年第1期3卷 1-7页

作者： Minghui Xu Yihao Guo Qin Hu Zehui Xiong Dongxiao Yu Xiuzhen Cheng School Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer and Information Science Indiana University-Purdue University IndianapolisUSA Information Systems Technology and Design Pillar Singapore University of Technology and DesignSingapore

Metaverse has rekindled human beings’desire to further break space-time barriers by fusing the virtual and real ***,security and privacy threats hinder us from building a utopia.A metaverse em-braces various techniques,while at the same time inheriting their pitfalls and thus exposing large attack ***,proposed in 2008,was regarded as a key building block of *** enables transparent and trusted computing environments using tamper-resistant decentralized ***,blockchain supports Decentralized Finance(DeFi)and Non-fungible Tokens(NFT)for ***-ever,the power of a blockchain has not been sufficiently *** this article,we propose a novel trustless architecture of blockchain-enabled metaverse,aiming to provide efficient resource integration and allocation by consolidating hardware and software *** realize our design objectives,we provide an On-Demand Trusted Computing Environment(OTCE)technique based on local trust ***,the architecture adopts a hypergraph to represent a metaverse,in which each hyper-edge links a group of users with certain *** the trust level of each user group can be evaluated based on graph analytics *** on the trust value,each group can determine its security plan on demand,free from interference by irrelevant ***,OTCEs enable large-scale and flexible application environments(sandboxes)while preserving a strong security guarantee.

关键词： Metaverse Blockchain Edge computing Trust

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A Quantal Response Analysis of Defender-Attacker Sequential Security Games 63

A Quantal Response Analysis of Defender-Attacker Sequential ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Azim, Md Reya Shad Abdallah, Mustafa Purdue University Indianapolis Electrical and Computer Engineering Department United States Purdue University in Indianapolis Computer and Information Technology Department United States

ISBN: (纸本)9798350316339

We explore a scenario involving two sites and a sequential game between a defender and an attacker, where the defender is responsible for securing the sites while the attacker aims to attack them. Each site holds a loss value for the defender when compromised, along with a probability of successful attack. The defender can reduce these probabilities through security investments at each site. The attacker's objective is to target the site that maximizes the expected loss for the defender, taking into account the defender's security investments. While previous studies have examined security investments in such scenarios, our work investigates the impact of bounded rationality exhibited by the defender, as identified in behavioral economics. Specifically, we consider quantal behavioral bias, where humans make errors in selecting efficient (pure) strategies. We demonstrate the existence of a quantal response equilibrium in our sequential game and analyze how this bias affects the defender's choice of optimal security investments. Additionally, we quantify the inefficiency of equilibrium investments under quantal decision-making compared to an optimal solution devoid of behavioral biases. We provide numerical simulations to validate our main findings. © 2024 IEEE.

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Exploring Machine Learning Methods to Identify Patterns in Students' Solutions to Programming Assignments 53

Exploring Machine Learning Methods to Identify Patterns in S...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Liu, Xiaojin Castellanos, Hugo Wiese, Lucas Magana, Alejandra J. Purdue University Department of Computer Science Department of Computer and Information Technology West LafayetteIN United States Purdue University Department of Computer and Information Technology West LafayetteIN United States School of Engineering Education Purdue University Department of Computer and Information Technology West LafayetteIN United States

ISBN: (纸本)9798350336429

technology and automation have become increasingly critical for organizations today, and programming has become an essential skill for all STEM majors to meet this demand. Graduates are expected to possess programming skills to meet the needs of the modern workforce. Acquiring programming skills is a challenging task, and institutions often struggle to provide adequate resources to meet the industry's demand for proficient computer programmers. To take steps toward better understanding programming challenges among undergraduate students in science disciplines, this study aims to characterize patterns in students' solutions to programming assignments over the course of a semester. With this, the goal is to characterize students' most common challenges and take steps toward providing automated feedback. Specifically, this study applies machine learning (ML) classification algorithms to analyze student artifacts from a college-level introductory Python programming course for science majors, including source code from labs, homework assignments, projects, and two live-coding exams. Data was collected as part of a semester-long course and pre-processed and de-identified. The researchers labeled the data, and relevant features were selected to prepare the data for training the ML algorithms. Various classification algorithms were trained, and the resulting ML models were evaluated for their accuracy. Then, the study deployed a quantitative research method to evaluate both the effectiveness of various ML models and the quality of the feedback the model could provide, such as efficiency and accuracy. The research results are expected to inform the development of machine-learning algorithms to provide higher-quality feedback mechanisms for students in introductory programming courses. In that manner, this study contributes to improving the quality of programming education. © 2023 IEEE.

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Adventitious Pulmonary Sound Detection: Leveraging SHAP Explanations and Gradient Boosting Insights 46

Adventitious Pulmonary Sound Detection: Leveraging SHAP Expl...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Shokouhmand, Shiva Rahman, Md Motiur Faezipour, Miad Bhatt, Smriti Purdue University School of Engineering Technology Electrical & Computer Engineering Technology West LafayetteIN47907 United States Purdue University Department of Computer and Information Technology West LafayetteIN47907 United States

ISBN: (纸本)9798350371499

Pulmonary illnesses are annually reported as highly prevalent. Patient outcomes can, however, be improved through the aid of automated processes for early diagnoses. This study aims to develop an automated method with a comprehensive explanation for diagnosing underlying adventitious sounds in respiratory diseases. For this purpose, stethoscope audio signal recordings are initially segmented to merely include either normal or adventitious sounds such as wheezing and crackling. A comprehensive feature set representing the temporal and spectral dynamics in the respiratory events is extracted to identify the respiratory condition. An extreme gradient-based boosting (XGBoost) model is used and evaluated on the ICBHI 2017 dataset recordings based on a five-fold cross-validation approach. To further increase the model explainability, shapley values are calculated and analyzed. Our predictive method suggests specificity, sensitivity, and ICBHI scores of 94.57%, 77.96%, and 86.27%, respectively, demonstrating superior results, outperforming the state-of-the-art techniques. It is also concluded that Mel-frequency cepstral coefficients (MFCC), spectral centroid, zero crossing rate, and signal intensity are the most consistent discriminating features within the adventitious sounds. Clinical relevance - This work contributes to the development of advanced smart digital stethoscopes and respiratory monitoring systems that can be used in clinical, telemedicine and personalized healthcare settings for early detection of breathing disorders or pulmonary conditions. © 2024 IEEE.

关键词： Lung cancer

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