Exploring IoT privacy compliance research: a topic modeling approach

It is our great pleasure to welcome you to the 2015 ACM Workshop on IoT Privacy, Trust, and Security -- IoTPTS'15. At a basic level, the Internet of Things (IoT) refers simply to networked devices, but the IoT vision consists of a complex ecosystem that ranges from cloud backend services and big-data analytics to home, public, industrial, and wearable sensor devices and appliances. Architectures for these systems are in the formative stages, and IoTPTS 2015 gives researchers and practitioners a unique opportunity ensure privacy, trust, and security are designed into these systems from the beginning. This is the inaugural year of the IoTPTS Workshop and the call for papers attracted 13 submissions worldwide from 12 countries and 4 continents. The final program contained 5 papers (representing an acceptance rate of 38%) and a keynote IoT Security and Privacy: Threats and Challenges by Dr. Yong Ho Hwang from the Software R&D Center at Samsung Electronics.

Internet of Things (IoT): From awareness to continued use

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

As the Internet of Things (IoT) revolutionizes intelligent environments, energy efficiency remains a critical challenge. Traditional IoT-based energy management systems rely on predefined rule-based automation, which lacks adaptability to dynamic environmental conditions. This study proposes an Agentic AIoT system that integrates AI Agents, Large Language Models (LLMs), and Digital Twin technology to enhance IoT decision-making and optimize energy consumption in smart homes. Unlike conventional IoT frameworks, our system dynamically analyzes contextual factors—including real-time environmental conditions, user behaviors, and external data sources (e.g., weather forecasts and occupancy schedules)—to generate optimal energy-saving decisions autonomously. The proposed system consists of three key components: (1) Information Modules, which aggregate multi-source data beyond traditional IoT sensor inputs and construct a Digital Twin model for real-time environment simulation; (2) AI Agents & LLMs, which process contextual data and generate adaptive energy-saving strategies through intelligent reasoning; and (3) Automated Decision Execution, which seamlessly integrates optimized actions into IoT operations. By incorporating Digital Twin technology, the system can simulate various energy consumption scenarios, predict future power demands, and refine decision-making through AI-driven iterative learning. Experimental results demonstrate that our AIoT system achieves a 47.77% reduction in energy consumption compared to non-IoT environments while enhancing automation, adaptability, and user-centric control. Unlike conventional rule-based IoT solutions, integrating Digital Twin with LLM-powered AI Agents enables real-time context-aware decision-making and predictive energy optimization. This study contributes to intelligent IoT applications, Digital Twin-driven energy management, and AI-enhanced automation. Future research will focus on refining AI-driven autonomous decision-making, improving Digital Twin’s predictive accuracy, and further quantifying the impact of LLM-powered AIoT systems on energy efficiency. Our findings highlight the trans-formative potential of LLM-integrated AIoT with Digital Twin technology in revolutionizing energy management for intelligent environments while ensuring seamless interoperability with existing IoT infrastructures.

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

The widespread adoption of artificial intelligence technologies has led to comprehensive transformations within organizations. One of the most critical areas affected by this transformation is human resources. In this regard, the study examines the publication performance, research topics, and temporal evolution of the literature on artificial intelligence in human resources management. In the study, 1.999 peer-reviewed publications obtained from the Scopus database over the past ten years were analyzed using the structural topic modeling method, which is a topic modeling approach. As a finding of the analysis, 19 topics were identified. Furthermore, the findings reveal that research on artificial intelligence in human resources management has demonstrated an exponential growth trend during the period of 2016–2025. In terms of topic proportions, the most frequently studied themes were identified as “Digital Transformation in Human Resources Management,” “Ethical Recruitment,” and “Chatbot Applications,” while “Knowledge Management,” “GenAI in Assesment” and “Talent Acquisition” were represented at comparatively lower levels. The trend analysis further shows that “Ethical Recruitment,” “Green Human Resources Management,” “Adoption of Innovation,” “Digital Transformation in HRM” and “GenAI in Evaluation” have exhibited consistent growth, whereas “Internet of Things,” “Agile Team Management,” “Decision Support,” “Artificial Neural Networks,” “Project Management,” and “Robotics” have followed a declining trajectory. On the other hand, it was also identified that “Machine Learning,” as a fundamental technology, and “Chatbot Applications,” as one of the most frequently studied themes, did not display statistically significant trends. Overall, the findings indicate that research on artificial intelligence in human resources management has been shifting its focus from purely technical aspects toward dimensions such as ethics, sustainability, and social impacts; however, this transformation has so far taken place only at a limited level.

The Internet of Things (IoT) has experienced constant growth in the number of devices deployed and the range of applications in which such devices are used. They vary widely in size, computational power, capacity storage, and energy. The explosive growth and integration of IoT in different domains and areas of our daily lives has created an Internet of Vulnerabilities (IoV). In the rush to build and implement IoT devices, security and privacy have not been adequately addressed. IoT devices, many of which are highly constrained, are vulnerable to cyber attacks, which threaten the security and privacy of users and systems. This survey provides a comprehensive overview of IoT in regard to areas of application, security architecture frameworks, recent security and privacy issues in IoT, as well as a review of recent similar studies on IoT security and privacy. In addition, the paper presents a comprehensive taxonomy of attacks on IoT based on the three-layer architecture model; perception, network, and application layers, as well as a suggestion of the impact of these attacks on CIA objectives in representative devices, are presented. Moreover, the study proposes mitigations and countermeasures, taking a multi-faceted approach rather than a per layer approach. Open research areas are also covered to provide researchers with the most recent research urgent questions in regard to securing IoT ecosystem.

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is enabling a new class of intelligent applications. Specifically, Large Language Models (LLMs) are emerging as powerful tools not only for natural language understanding but also for enhancing IoT security. However, the integration of these computationally intensive models into resource-constrained IoT environments presents significant challenges. This paper provides an in-depth examination of how LLMs can be adapted to secure IoT ecosystems. We identify key application areas, discuss major challenges, and propose optimization strategies for resource-limited settings. Our primary contribution is a novel collaborative embeddings offloading mechanism for IoT intrusion detection named SEED (Semantic Embeddings for Efficient Detection). This system leverages a lightweight, fine-tuned BERT model, chosen for its proven contextual and semantic understanding of sequences, to generate rich network embeddings at the edge. A compact neural network deployed on the end-device then queries these embeddings to assess network flow normality. This architecture alleviates the computational burden of running a full transformer on the device while capitalizing on its analytical performance. Our optimized BERT model is reduced by approximately 90% from its original size, now representing approximately 41 MB, suitable for the Edge. The resulting compact neural network is a mere 137 KB, appropriate for the IoT devices. This system achieves 99.9% detection accuracy with an average inference time of under 70 ms on a standard CPU. Finally, the paper discusses the ethical implications of LLM-IoT integration and evaluates the resilience of LLMs in dynamic and adversarial environments.

The Internet of Things (IoT) are being enthusiastically adopted by consumers. By the year 2020 the sum of 31 billon IoT devices will be deployed globally. Subsequent as the IoT device landscape is expanding at such speed, so does the threat landscape and vulnerabilities it introduces increases. Thus, making IoT devices easily prone to attacks or to be used to for launching attacks at large economical scale and society is seeing a growth in the scale and frequencies of these attacks. The large scale of attacks and frequency have caught global attention and causing governments to take the security and privacy threats of IoT very seriously and the UK government amongst others are now turning these concerns into actionable measures by considering ways of protecting consumers against the vulnerabilities and threats of IoT. It is part of these actionable measures that the NCSC (National Cyber Security Centre) recently published in a report about the new laws being proposed by the government to strengthen IoT devices. This chapter will look at the IoT security threats and privacy issues, it will explore whether the growing concern of the government to protect consumer has a foundation by investigating consumers awareness and attitude towards IoT security threats and privacy issues and propose a framework to facilitate the introduction of the new initiative of the government to bring in laws to govern IoT products thereby shifting the responsibility of the security threats to the manufacturers and away from the consumer.

The convergence of the Internet of Things (IoT), fog computing, and block chain technology has revolutionized contemporary digital ecosystems by making data management decentralized, efficient, and scalable. Nonetheless, this integration poses serious security and privacy threats. IoT devices produce enormous amounts of sensitive data, making them easy targets for cyber attacks, unauthorized access, and data breaches. Classic cloud-based solutions tend to be challenged by latency, bandwidth limitations, and centralized attacks, making the use of fog computing inevitable to decentralize data processing for better responsiveness and decreased dependency on remote cloud servers. While fog computing offers advantages, it also poses new security threats through possible insider attacks and data integrity issues. Block chain technology solves these problems by presenting a tamper-resistant, decentralized ledger that improves trust, security, and transparency for IoT networks. Block chain, via cryptographic authentication, consensus protocols, and smart contracts, ensures the secure transaction of data while alleviating risks against unauthorized data manipulation. Scalability, computational complexity, and energy efficiency are still challenges that need to be met for widespread IoT adoption. This chapter discusses the complex security and privacy problems in IoT, fog, and blockchain networks and examines current threats and countermeasures. Advanced cryptographic methods, secure communication schemes, and access control techniques for strengthening the security of these connected systems are mentioned. It further presents future research directions and trends that will improve a safer, more efficient, and privacy-augmented digital infrastructure.

Recently, some statistical topic modeling approaches based on LDA have been applied in the field of supervised document classification, where the model generation procedure incorporates prior knowledge to improve the classification performance. However, these customizations of topic modeling are limited by the cumbersome derivation of a specific inference algorithm for each modification. In this paper, we propose a new supervised topic modeling approach for document classification problems, Neural Labeled LDA (NL-LDA), which builds on the VAE framework, and designs a special generative network to incorporate prior information. The proposed model can support semi-supervised learning based on the manifold assumption and low-density assumption. Meanwhile, NL-LDA has a consistent and concise inference method while semi-supervised learning and predicting. Quantitative experimental results demonstrate our model has outstanding performance on supervised document classification relative to the compared approaches, including traditional statistical and neural topic models. Specially, the proposed model can support both single-label and multi-label document classification. The proposed NL-LDA performs significantly well on semi-supervised classification, especially under a small amount of labeled data. Further comparisons with related works also indicate our model is competitive with state-of-the-art topic modeling approaches on semi-supervised classification.

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

Privacy leakage of Internet of Things (IoT) has become a great challenge with the popularity of IoT services through mobile networks, such as smart homes, wearables, and healthcare. While previous work summarized general structures to analyze IoT privacy and provide case studies of specific devices or scenarios, it is still challenging to conduct a comprehensive and systematic quantification study of large-scale IoT privacy leakage in real world. To combine systematic analyses with real-world measurements, we provide a method to quantify IoT privacy leakage on a large-scale mobile network traffic data set containing 47651 IoT devices. We generate privacy fingerprints and attribute them to a privacy quantification framework. The framework is constructed based on the semantics of multiple privacy sensitive markers selected from the traffic along with the involved network entity types in IoT (i.e., user, device, and platform), and the fingerprints are generated from sensitive information extracted in the traffic via their markers. Our quantification shows that IoT users, devices, and platforms have considerable risks, respectively. Moreover, IoT devices have a larger scale of privacy leakage than users and platforms, and they perform different daily patterns on privacy leakage following their working conditions. In addition, we present three case studies on the leakage of location information, application calling, and voice service, which illustrate that a third party can profile a network entity in both cyberspace and physical space.

The Internet of Things (IoT) is the current evolutionary paradigm of networking and the key driving force toward a smart world. Although privacy in the IoT is highly regarded to ensure the protection of users and personal information from the perspective of individual or cooperative users, it's insufficiently studied. As members of the always-connected paradigm of the massive IoT world, people can scarcely control the disclosure of their personal information. The biggest challenge is to allow users to experience the best utilization of IoT-based products and services with the fewest privacy threats and failures. This article provides a holistic view of the challenges of and issues related to preserving IoT privacy, as well as the existing solutions. Privacy by design (PbD) is identified as the key solution for many IoT privacy issues. The article also discusses hot topics in IoT privacy and future research directions.

The Internet of Things (IoT) has witnessed rapid proliferation, revolutionizing the way we interact with technology in various domains of life. This ubiquitous network of interconnected devices, however, presents serious challenges concerning privacy and security. As IoT devices gather vast amounts of sensitive data and share it across networks, ensuring the protection of user privacy and guarding against potential security breaches becomes paramount. This abstract delves into the primary concerns surrounding IoT privacy and security, addressing issues such as data collection, user consent, data ownership, and vulnerability to cyber-attacks. Furthermore, it explores potential solutions, including encryption, blockchain technology, and the implementation of robust authentication mechanisms to safeguard IoT ecosystems. Acknowledging the significance of effective regulation and industry collaboration, this abstract concludes by emphasizing the need for continued research and proactive measures to address the evolving landscape of IoT privacy and security challenges.