Nowadays, with the rapid development of intelligent technology, it is urgent to effectively prevent infectious diseases and ensure people's privacy. The present work constructs the intelligent prevention system of infectious diseases based on edge computing by using the edge computing algorithm, and further deploys and optimizes the privacy information security defense strategy of users in the system, controls the cost, constructs the optimal conditions of the system security defense, and finally analyzes the performance of the model. The results show that the system delay decreases with the increase of power in the downlink. In the analysis of the security performance of personal privacy information, it is found that six different nodes can maintain the optimal strategy when the cost is minimized in the finite time domain and infinite time domain. In comparison with other classical algorithms in the communication field, when the intelligent prevention system of infectious diseases constructed adopts the best defense strategy, it can effectively reduce the consumption of computing resources of edge network equipment, and the prediction accuracy is obviously better than that of other algorithms, reaching 83%. Hence, the results demonstrate that the model constructed can ensure the safety performance and forecast accuracy, and achieve the best defense strategy at low cost, which provides experimental reference for the prevention and detection of infectious diseases in the later period.
Despite the emergence of unique opportunities for social-industrial growth and development resulting from the use of the Internet of Things (IoT), lack of a well-posed IoT governance will cause serious threats on personal privacy, public safety, industrial security, and dubious data gathering by unauthorized entities. Furthermore, adopting a systemic governance approach, particularly for the IoT innovation system, requires a precise clarification on the concept and scope of IoT governance. In this study, by employing the Structural Equation Modeling (SEM) approach, the role of governance in the Iran IoT innovation system is investigated. Contacting respondents across the seven industries, including Information and Communication Technology (ICT), Healthcare, Transportation, Oil and Gas, Energy, Agriculture, and Banking over the course of three months, the authors performed statistical analysis on 319 fulfilled questionnaires using SPPS and Smart PLS software. Findings show that all IoT-related TIS processes have been affected by IoT governance functions. The main result of this study is the proposition of particular governance functions, including policy-making, regulation, facilitation, and service provision with more notable impact on the indicators of the key processes in the IoT-based TIS.
Face images, as an information carrier, are naturally weak in privacy. If they are collected and analyzed by malicious third parties, personal privacy will leak, and many other unmeasurable losses will occur. Differential privacy protection of face images is mainly being studied under non-interactive frameworks. However, the ε-effect impacts the entire image under these frameworks. Besides, the noise influence is uniform across the protected image, during the realization of the Laplace mechanism. The differential privacy of face images under interactive mechanisms can protect the privacy of different areas to different degrees, but the total error is still constrained by the image size. To solve the problem, this paper proposes a non-global privacy protection method for sensitive areas in face images, known as differential privacy of landmark positioning (DPLP). The proposed algorithm is realized as follows: Firstly, the active shape model (ASM) algorithm was adopted to position the area of each face landmark. If the landmark overlaps a subgraph of the original image, then the subgraph would be taken as a sensitive area. Then, the sensitive area was treated as the seed for regional growth, following the fusion similarity measurement mechanism (FSMM). In our method, the privacy budget is only allocated to the seed; whether any other insensitive area would be protected depends on whether the area exists in a growing region. In addition, when a subgraph meets the criterion for merging with multiple seeds, the most reasonable seed to be merged would be selected by the exponential mechanism. Experimental results show that the DPLP algorithm satisfies ε-differential privacy, its total error does not change with image size, and the noisy image remains highly available.
In this article, we present results on research performed in the TEMPEST domain, which studies the electromagnetic disturbances generated unintentionally by electronic equipment as well as the methods to protect the information processed by this equipment against these electromagnetic phenomena. The highest vulnerability of information leakage is attributed to the display video signal from the TEMPEST domain perspective. Examples of far-range propagation on a power line of this type of disturbance will be illustrated for the first time. Thus, the examples will highlight the possibility of recovering processed information at distances of 1, 10 and 50 m. There are published articles studying electromagnetic disturbances generated by electronic equipment propagating on power cables of such equipment but no studies on their long-distance propagation. Our research aims to raise awareness in the scientific community and the general public of the existence of such vulnerabilities that can compromise confidential or sensitive information that can make the difference between success or failure in the business sector, for example, or can harm personal privacy, which is also important for us all. Countermeasures to reduce or even eliminate these threats will also be presented based on the analysis of the signal-to noise-ratio recorded during our research.
With the development of big data technology, the privacy concerns of face recognition have become the most critical social issue in the era of information sharing. Based on the perceived ease of use, perceived usefulness, social cognition, and cross-cultural aspects, this study analyses the privacy of face recognition and influencing factors. The study collected 518 questionnaires through the Internet, SPSS 25.0 was used to analyze the questionnaire data as well as evaluate the reliability of the data, and Cronbach’s alpha (α coefficient) was used to measure the data in this study. Our findings demonstrate that when users perceive the risk of their private information being disclosed through face recognition, they have greater privacy concerns. However, most users will still choose to provide personal information in exchange for the services and applications they need. Trust in technology and platforms can reduce users’ intention to put up guards against them. Users believe that face recognition platforms can create secure conditions for the use of face recognition technology, thus exhibiting a higher tendency to use such technology. Although perceived ease of use has no significant positive impact on the actual use of face recognition due to other external factors, such as accuracy and technology maturity, perceived usefulness still has a significant positive impact on the actual use of face recognition. These results enrich the literature on the application behavior of face recognition and play an important role in making better use of face recognition by social individuals, which not only facilitates their daily life but also does not disclose personal privacy information.
The article examines the existing scientific approaches to the category of "intangible benefits", at the same time it compares the content of this concept with the term "personal non-property rights". The relevance of the theme is beyond doubt, since intangible benefits are protected from encroachments specifically by recovery of compensation for moral injury. This institution provides for the possibility of compensation for non-material damage, in practice, the most widely used method is the one that involves action demand with a statement of claim. Consequently, there is an obvious need to study the issues of legal protection and protection of intangible benefits from the point of view of the analysis of judicial and law enforcement practice. The purpose of the study is to analyze the definition of "intangible benefits" formalized in civil legislation and to identify the non-mandatory beginnings of its practical application in civil circulation. The methodological basis was the comparative law, formal legal, logical, dialectical and other methods of scientific research. Attention is drawn to certain elements of intangible benefits – personal inviolability, personal privacy and inviolability of the home, personal data, business reputation, etc. Much attention is paid to disclosing the scientific and legal concept of intangible goods, as a result of which the author comes to the conclusion that it is impossible to unambiguously understand the substance of intangible goods, in connection with which various approaches are proposed to understanding and disclosing the content of the desired category of "intangible goods" in the Russian civil law. Improving the regulatory framework which regulates the protection of non-property rights of legal entities should be considered one of the goals in the development of modern civil legislation.
The balancing function, between worker and employer, of the fundamental rights in the field of the labor relationship is analyzed, while emphasizing that the right to privacy is not an unlimited right, but that it can yield to other constitutional rights. Likewise, the right to the protection of personal data is studied – distinguishing it from the right to personal privacy – which aims to guarantee the freedom of the individual in relation to their self-determination regarding the processing of their personal data by third parties.
The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.
In this paper I discuss the political value of the right to privacy. The classical accounts of privacy do not differentiate between privacy as the right of a citizen against other citizens vs. the right to privacy as the right against the state or the government. I shall argue that this distinction should be made, since the new context of the privacy debate has surpassed the historical frames in which the intelligence methods used by governments were comparable to those available to individuals. I also present cases in which political privacy serves as an instrument of protecting important collective agendas exceeding the context of personal privacy. I argue that due to its function, political privacy should be considered a necessary element of democratic governance with the rule of law, imposing legal bounds on governments’ discretionary actions.
Advances in web technology and the widespread use of smartphones and PCs have proven that it is possible to optimize various services using personal data, such as location information and search history. While considerations of personal privacy and legal aspects lead to situations where data are monopolized by individual services and companies, a replication crisis has been pointed out for the data of laboratory experiments, which is challenging to solve given the difficulty of data distribution. To ensure distribution of experimental data while guaranteeing security, an online experiment platform can be a game changer. Current online experiment platforms have not yet considered improving data distribution, and it is currently difficult to use the data obtained from one experiment for other purposes. In addition, various devices such as activity meters and consumer-grade electroencephalography meters are emerging, and if a platform that collects data from such devices and tasks online is to be realized, the platform will hold a large amount of sensitive data, making it even more important to ensure security. We propose GO-E-MON, a service that combines an online experimental environment with a distributed personal data store (PDS), and explain how GO-E-MON can realize the reuse of experimental data with the subject’s consent by connecting to a distributed PDS. We report the results of the experiment in a groupwork lecture for university students to verify whether this method works. By building an online experiment environment integrated with a distributed PDS, we present the possibility of integrating multiple experiments performed by different experimenters—with the consent of individual subjects—while solving the security issues.