Figure - available from: Information Systems Frontiers
This content is subject to copyright. Terms and conditions apply.
Source publication
The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types ge...
Similar publications
In this paper, we present RedCASTLE, a practically applicable solution for Edge-based k_s-anonymization of IoT streaming data in Node-RED. RedCASTLE builds upon a pre-existing, rudimentary implementation of the CASTLE algorithm and significantly extends it with functionalities indispensable for real-world IoT scenarios. In addition, RedCASTLE provi...
Citations
... Ren et al. [41] addressed privacy challenges in the IoT and proposed techniques for preserving privacy with different types of IoT data. Their methods included data stream anonymization using k-anonymity and privacy-enhancing techniques for continuous and media data. ...
The integration of the IoT into healthcare opens new horizons while introducing ethical and legal challenges to preserving patients’ privacy. This paper provides a comprehensive review of privacy-preserving mechanisms in IoT-based healthcare systems, analyzing key challenges such as secure data transmission, decentralized processing, privacy-preserving analytics, and user-centric control. We classify existing privacy solutions into a structured comparative framework, highlighting their integration strategies, security measures, and technical implementations in scalable architectures. Additionally, we discuss emerging trends and open research challenges that require further exploration. This study is a valuable reference for researchers, practitioners, and policymakers seeking to develop and enhance privacy-preserving solutions in IoT-based healthcare environments.
... Those solutions focus on improving and optimizing cryptographic algorithms to secure data and preserve its privacy. Authors in [170] propose a stream data anonymization method based on k-anonymity for data collected by IoT devices. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the value of the data. ...
This thesis aims to propose a reliable e-health architecture that addresses IoT
challenges and constraints. To achieve reliability, three crucial aspects were
addressed. First, hybrid anomaly detection since data sources can lead to erro-
neous data or anomalies (data that deviate and become falsified) that should be
detected using machine learning algorithms. Secondly, these anomalies should
be alerted in real-time; therefore, scalability and efficiency for fine-grained alerts
are important keys to achieving reliability. Using big data systems, the stream
of data will be consumed by multiple end users in near real time. Thirdly,
it follows that one of the objectives of reliability is to implement secure and
privacy-preserving solutions that ensure the confidentiality of sensitive data
from being altered. Encryption techniques must be implemented while con-
sidering the characteristics of IoT components. Ultimately, compliance with
regulatory standards for health data, such as HIPAA and GDPR, was consid-
ered to verify the reliable IoT healthcare architecture.
Keywords: Internet of Things, Healthcare, Reliability, Anomaly Detec-
tion, Machine learning, Scalability, Efficiency, Big data, Security and Privacy,
Compliance.
... Basis of AI-Driven Data Analytics in Industry 4.0[12]. ...
In Industry 4.0, data is enormously exchanged through wireless devices. Ensuring data privacy and protection is vital. The proposed review paper explores how AI techniques can safeguard sensitive information and ensure compliance. It explores fundamental technologies such as cyber-physical systems and the complex application of AI in analytics and predictive maintenance. The issues with data security are then emphasized and privacy concerns resulting from human-machine interaction are shown. Regulatory frameworks that direct enterprises are touted as essential defenses, coupled with AI-powered solutions and privacy-preserving tactics. Examples from everyday life highlight the constant battle for equilibrium. The review continues with a look ahead to future developments in interdisciplinary research and ethical issues that will influence Industry 4.0's responsible growth. In essence, this paper synthesizes a nuanced understanding of the sophisticated challenges surrounding privacy and data protection within Industry 4.0, underscoring the pivotal role of AI as a custodian of sensitive information and offering an indispensable resource for professionals, policymakers, and researchers navigating the intricate and evolving terrain of Industry 4.0 with technical precision and ethical responsibility.
... Various aspects of privacy preservation in healthcare have been studied by the related work. Ren et al. focused on privacy-enhancing techniques on the Internet of Things (IoT) and the role of data anonymization in addressing privacy concerns within IoT ecosystems [10], while Dimopoulou et al. conducted research on the challenges for securing health information in mobile environments [11]. Louassef et al., provided a new taxonomy of privacy preservation techniques in healthcare systems, mentioning also the need for a better understanding of the techniques [12]. ...
... Next, Sangaiah et al., investigated entropy strategies for privacy preservation in healthcare [20]. In another work [10], the researchers addressed the challenges of data sharing and the importance of observing quasiidentifiers. More specifically, the authors introduced a Python library that implemented various anonymization techniques for assessing the level of anonymity in a dataset. ...
In modern healthcare systems, data sources are highly integrated, and the privacy challenges are becoming a paramount concern. Despite the critical importance of privacy preservation in safeguarding sensitive and private information across various domains, there is a notable deficiency of learning and training material for privacy preservation. In this research, we present a k-anonymity algorithm explicitly for educational purposes. The development of the k-anonymity algorithm is complemented by seven validation tests, that have also been used as a basis for constructing five learning scenarios on privacy preservation. The outcomes of this research provide a practical understanding of a well-known privacy preservation technique and extends the familiarity of k-anonymity and the fundamental concepts of privacy protection to a broader audience.
... Those solutions are using several techniques such as encryption, anonymization, and access control. Authors in [10] propose a stream data anonymization method based on k-anonymity for data collected by IoT devices. ...
Data privacy in the Internet of Things (IoT) remains a challenging topic in all industries, including healthcare services. The introduction of new data privacy regulations: the Health Insurance Portability and Accountability Act (HIPAA) and the European General Data Protection Regulation (GDPR) enables users to control how their data is accessed and processed, requiring consent from users before any data collection from smart devices or sensors. Consequently, the implementation of these regulations in IoT infrastructures is still a major concern for all researchers. Since these laws don’t only apply to doctors and nurses, IT personnel must comply too. In this work, a novel IoT architecture is proposed to protect users’ privacy and comply with both regulations using edge computing and encryption techniques for healthcare infrastructure.
... These devices enable the monitoring of athletes' physical parameters based on Wireless Body Area Networks technologies, allowing for macro-level analysis in the fields of sports and health. 40 These devices can measure a wide spectrum of parameters, such as maximum 36 Sun et al., 54 and Kofahi et al. 55 Electronic health record keeping Sun et al., 54 Nahavandi et al., 56 and Yang et al. 57 Sports nutrition and health management system Wang 58 and Xiong and Li 59 Smart body composition assessment Ren et al., 60 De Fazio et al., 61 and Ghosh and Sundaravdivel 62 Athlete protection Nekit et al., 63 Zhen et al., 64 and Elgazzar et al. 65 Simulation and prediction of sports injuries Yang et al., 66 Ding et al., 67 Monitoring the structural health of equipment and sports venues Baker et al. 120 Location tracking in smart stadium system, real-time data collection and location tracking in the stadium Zhao et al. 121 Optimizing the consumer goods management system for sports products ...
The current research aims to answer two significant research questions: What are the dimensions of the application of the Internet of Things in the sports industry? And what are the components of each aspect of using the Internet of Things in the sports industry? Also, this study responds to the development of literature on Industry 4.0 technologies in the sports industry. The current research used the meta-synthesis method and the seven-step method of Sandelowski and Barroso to review 131 related qualitative studies. The results of the surveys include 10 dimensions and 79 indicators of Internet of Things applications in the sports industry, including health management, safety and security management, optimization of management processes, fan experience, management of sports facilities and venues, improving performance (of referees and athletes), marketing and media, smart equipment, and sports tourism. The applications obtained from Internet of Things technology can be used in the determined dimensions for developing and improving sports. Finally, it is important to emphasize the importance of continuing to deepen knowledge about the Internet of Things as well as the new technologies available in the fourth generation of the industrial revolution and its applications in the field of sports. Research aimed at studying the potential of these technologies in the sports industry and its management is necessary.
... In while the current model shows promise, it represents a starting point for ongoing research and improvements in IoT device security classification. The combination of machine learning and domain-specific knowledge holds the potential to advance the state of IoT security (Ren, W., et al., 2021). ...
The rapid proliferation of IoT (Internet of Things) devices has ushered in an era of unprecedented connectivity and automation. The widespread adoption has also exposed vulnerabilities, necessitating robust security and privacy measures. This research presents a comprehensive study focused on enhancing IoT device and network security and privacy through empirical investigation and advanced machine learning techniques. Commencing with an exhaustive literature review, it was assessed, the evolving landscape of IoT security threats, solutions, and identified research gaps. Building upon the foundation, it was designed and rigorously evaluated a machine learning-based classification model tailored for IoT device security. Utilizing a meticulously crafted simulated dataset mirroring real-world IoT features, our model undergoes comprehensive performance evaluations. Metrics include accuracy, precision, recall, F1 score, and ROC analysis. Our findings reveal a nuanced performance profile, shedding light on the model's capability to accurately classify IoT devices as Secure or Vulnerable. Precision-recall trade-offs, emphasizing the need for a judicious balance to mitigate false positives and false negatives was investigated. The critical role of feature engineering and model refinement, pointing to areas for future research and optimization. This research contributes to the burgeoning field of IoT security by employing machine learning as a proactive tool for fortifying IoT device and network security. Our findings advocate for a strategic approach to secure IoT ecosystems, ensuring data integrity and privacy in the face of evolving threats. As IoT devices continue to proliferate across industries, this research serves as a foundation for innovative strategies and ongoing investigations to harness the full potential of secure IoT environments while addressing multifaceted challenges in the ever-evolving IoT landscape.
... Although most techniques include changes to the data value, there are some distinctions amongst them. Data swapping techniques swap the data without affecting data values, while other techniques imply changes in the data values [10]. Since anonymisation techniques [11][12][13][14][15][16][17] are usually applied to the data, this technique is most suitable when the RS sentences' selected attribute is still unknown. ...
... In Ref. [30], a lightweight encryption scheme was proposed for secure IoT devices using a piecewise linear chaotic map and a grain keystream generator. In Ref. [31], the authors addressed three primary data types generated within the IoT context. They commenced by introducing a stream data anonymization approach founded on the principle of k-anonymity, specially tailored for securing data collected by IoT devices. ...
Integrating Internet of Things (IoT) technologies in art design has created new possibilities for artists to create immersive and interactive experiences. However, data collection, analysis, and utilization in IoT art installations raise significant security and privacy concerns. Additionally, incorporating differential privacy techniques in IoT art installations poses optimization challenges. This paper explores optimizing differential privacy budgets based on deep learning in IoT art installations. By leveraging deep learning models, privacy budgets can be dynamically allocated to preserve individual privacy while maintaining the aesthetic integrity of the artwork. In light of this, a deep learning-based differential privacy budget optimization strategy for IoT art installations is suggested. This method adaptively distributes various budgets by the iterative change law of parameters. A regularization term is provided to limit the disturbance term to avoid the issue of excessive noise. This stops the neural network from overfitting and also assists in learning the model's salient characteristics. The capacity of the model to generalize is effectively improved by the suggested strategy, according to experiments. The accuracy difference between the model trained with noise and the model trained with original data is less than 0.5% as the number of iterations increases. Therefore, the proposed method can protect the user's privacy, effectively ensure the model's availability, and achieve the balance between privacy and availability. This accuracy ensures that the installation functions as intended and delivers the desired aesthetic impact, enabling artists to convey their artistic message effectively.
... We used data anonymization [36] and advanced encryption standard (AES) [37] to anonymize and encrypt the metadata set in Table 3. Then, we evaluated the accuracy of each classification algorithm for online user identification. ...
Machine learning algorithms, such as KNN, SVM, MLP, RF, and MLR, are used to extract valuable information from shared digital data on social media platforms through their APIs in an effort to identify anonymous publishers or online users. This can leave these anonymous publishers vulnerable to privacy-related attacks, as identifying information can be revealed. Twitter is an example of such a platform where identifying anonymous users/publishers is made possible by using machine learning techniques. To provide these anonymous users with stronger protection, we have examined the effectiveness of these techniques when critical fields in the metadata are masked or encrypted using tweets (text and images) from Twitter. Our results show that SVM achieved the highest accuracy rate of 95.81% without using data masking or encryption, while SVM achieved the highest identity recognition rate of 50.24% when using data masking and AES encryption algorithm. This indicates that data masking and encryption of metadata of tweets (text and images) can provide promising protection for the anonymity of users’ identities.
