Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 7.365
Filtrar
1.
Rev. SPAGESP ; 22(2): 19-32, jul.-dez. 2021. tab
Artículo en Portugués | LILACS, Index Psicología - Revistas | ID: biblio-1340810

RESUMEN

O presente estudo parte de uma investigação mais ampla sobre a vergonha e o pudor na intimidade da família e tem por objetivo investigar a percepção do pudor em famílias com filhos adolescentes. Realizou-se uma pesquisa qualitativa na qual foram entrevistados oito sujeitos do segmento socioeconômico médio. Os resultados foram analisados de acordo com o método de análise de conteúdo, vertente temático-categorial e discutidos a partir do referencial teórico da psicanálise e das ciências sociais. Considerando o objetivo do presente estudo, apresentamos e discutimos as categorias: estranhamento do pudor; transmissão do pudor: um legado em questão; novos rumos da intimidade no lar e imagem no espaço público: protegendo a vergonha dos filhos. Os resultados apontam que os participantes atribuem ao pudor um significado de intimidade corporal e sexual transmitido pelas gerações anteriores e com pouca expressão na atual interação familiar. Concluímos que a transmissão geracional do pudor vem sofrendo uma descontinuidade, tendo em vista a maior proximidade afetiva com os filhos e seus reflexos na transformação da intimidade familiar.


ABSTRACT The present study is part of a broader investigation of shame and modesty in the intimacy of the family and aims to investigate the perception of modesty in families with adolescent children. Qualitative research was conducted in which eight 8 subjects from the middle socioeconomic segment were interviewed. According to the content analysis method, the results were analyzed, thematic-categorical, and discussed from the theoretical framework of psychoanalysis and social sciences. Considering the objective of the present study, we present and discuss the categories: the strangeness of modesty; transmission of modesty: a legacy in question; new directions of intimacy in the home and image in the public space: protecting the shame of children. The results show that the participants attribute to modesty a meaning of bodily and sexual intimacy transmitted by previous generations and little expression in the current family interaction. We conclude that the generational transmission of modesty has suffered a discontinuity, given the greater affective proximity to the children and their reflexes in the transformation of family intimacy.


RESUMEN El presente estudio, parte de una investigación más amplia sobre la vergüenza y la modestia en la intimidad de la familia y tiene como objetivo investigar la percepción de la modestia en familias con hijos adolescentes. Se realizó una investigación cualitativa en la que se entrevistó a 8 sujetos del segmento socioeconómico medio. Los resultados fueron analizados según el método de análisis de contenido, temático-categórico y discutidos desde el marco teórico del psicoanálisis y las ciencias sociales. Teniendo en cuenta el objetivo del presente estudio, presentamos y discutimos las categorías: extrañeza del pudor; transmisión del pudor: un legado en cuestión; nuevas direcciones de intimidad en el hogar e imagen en el espacio público: protegiendo la vergüenza de los niños. Los resultados muestran que los participantes atribuyen al pudor un significado de intimidad corporal y sexual transmitido por generaciones anteriores, y con escasa expresión en la interacción familiar actual. Concluimos que la transmisión generacional del pudor ha sufrido una discontinuidad, ante la mayor proximidad afectiva con los hijos y sus reflejos en la transformación de la intimidad familiar.


Asunto(s)
Humanos , Masculino , Femenino , Adolescente , Valores Sociales , Privacidad , Sexualidad , Relaciones Familiares , Moral
2.
Stud Health Technol Inform ; 287: 3-7, 2021 Nov 18.
Artículo en Inglés | MEDLINE | ID: mdl-34795068

RESUMEN

Federated learning has a great potential to create solutions working over different sources without data transfer. However current federated methods are not explainable nor auditable. In this paper we propose a Federated data mining method to discover association rules. More accurately, we define what we consider as interesting itemsets and propose an algorithm to obtain them. This approach facilitates the interoperability and reusability, and it is based on the accessibility to data. These properties are quite aligned with the FAIR principles.


Asunto(s)
Algoritmos , Privacidad , Minería de Datos , Proyectos de Investigación
3.
Stud Health Technol Inform ; 287: 50-54, 2021 Nov 18.
Artículo en Inglés | MEDLINE | ID: mdl-34795078

RESUMEN

To handle genomic information while supporting FAIR principles, we present GIPAMS, a modular architecture. GIPAMS provides security and privacy to manage genomic information by means of several independent services and modules that interact among them in an orchestrated way. The paper analyzes how some security and privacy aspects of the FAIRification process are covered by the GIPAMS platform.


Asunto(s)
Seguridad Computacional , Privacidad , Confidencialidad , Genómica
6.
Sensors (Basel) ; 21(22)2021 Nov 09.
Artículo en Inglés | MEDLINE | ID: mdl-34833529

RESUMEN

Smart mirrors are devices that can display any kind of information and can interact with the user using touch and voice commands. Different kinds of smart mirrors exist: general purpose, medical, fashion, and other task specific ones. General purpose smart mirrors are suitable for home environments but the exiting ones offer similar, limited functionalities. In this paper, we present a general-purpose smart mirror that integrates several functionalities, standard and advanced, to support users in their everyday life. Among the advanced functionalities are the capabilities of detecting a person's emotions, the short- and long-term monitoring and analysis of the emotions, a double authentication protocol to preserve the privacy, and the integration of Alexa Skills to extend the applications of the smart mirrors. We exploit a deep learning technique to develop most of the smart functionalities. The effectiveness of the device is demonstrated by the performances of the implemented functionalities, and the evaluation in terms of its usability with real users.


Asunto(s)
Emociones , Voz , Humanos , Privacidad
7.
Sensors (Basel) ; 21(22)2021 Nov 12.
Artículo en Inglés | MEDLINE | ID: mdl-34833591

RESUMEN

Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework.


Asunto(s)
Aprendizaje Profundo , Computadoras de Mano , Ejercicio Físico , Privacidad , Teléfono Inteligente
8.
Sensors (Basel) ; 21(22)2021 Nov 17.
Artículo en Inglés | MEDLINE | ID: mdl-34833723

RESUMEN

With the emerging growth of digital data in information systems, technology faces the challenge of knowledge prevention, ownership rights protection, security, and privacy measurement of valuable and sensitive data. On-demand availability of various data as services in a shared and automated environment has become a reality with the advent of cloud computing. The digital fingerprinting technique has been adopted as an effective solution to protect the copyright and privacy of digital properties from illegal distribution and identification of malicious traitors over the cloud. Furthermore, it is used to trace the unauthorized distribution and the user of multimedia content distributed through the cloud. In this paper, we propose a novel fingerprinting technique for the cloud environment to protect numeric attributes in relational databases for digital privacy management. The proposed solution with the novel fingerprinting scheme is robust and efficient. It can address challenges such as embedding secure data over the cloud, essential to secure relational databases. The proposed technique provides a decoding accuracy of 100%, 90%, and 40% for 10% to 30%, 40%, and 50% of deleted records.


Asunto(s)
Seguridad Computacional , Registros Electrónicos de Salud , Nube Computacional , Confidencialidad , Privacidad , Tecnología
9.
Sensors (Basel) ; 21(22)2021 Nov 18.
Artículo en Inglés | MEDLINE | ID: mdl-34833732

RESUMEN

Internet of Things (IoT) applications bring evolved and intelligent services that can help improve users' daily lives. These applications include home automation, health care, and smart agriculture. However, IoT development and adoption face various security and privacy challenges that need to be overcome. As a promising security paradigm, context-aware security enables one to enforce security and privacy mechanisms adaptively. Moreover, with the advancements in edge computing, context-aware security services can dynamically be placed close to a user's location and enable the support of low latency communication and mobility. Therefore, the design of an adaptive and decentralized access control mechanism becomes a necessity. In this paper, we propose a decentralized context-aware authorization management as a service based on the blockchain. The proposed architecture extends the Authentication and Authorization for Constrained Environments (ACE) framework with blockchain technology and context-awareness capabilities. Instead of a classic Open Authorization 2.0 (OAuth) access token, it uses a new contextual access token. The evaluation results show our proposition's effectiveness and advantages in terms of usability, security, low latency, and energy consumption.


Asunto(s)
Cadena de Bloques , Internet de las Cosas , Seguridad Computacional , Atención a la Salud , Privacidad
10.
Sensors (Basel) ; 21(22)2021 Nov 19.
Artículo en Inglés | MEDLINE | ID: mdl-34833792

RESUMEN

Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.


Asunto(s)
Algoritmos , Nube Computacional , Aprendizaje Automático , Privacidad
11.
Comput Intell Neurosci ; 2021: 7156420, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34840562

RESUMEN

Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyperparameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely, genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyperparameters and genetically modifies the parameters clusterwise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyperparameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data. An accuracy of 99.79% is observed in the MNIST dataset and 76.88% in CIFAR-10 dataset with only 10 training rounds.


Asunto(s)
Algoritmos , Aprendizaje Automático , Análisis por Conglomerados , Comunicación , Humanos , Privacidad
12.
J Med Internet Res ; 23(11): e23059, 2021 11 16.
Artículo en Inglés | MEDLINE | ID: mdl-34783672

RESUMEN

BACKGROUND: Fitness trackers allow users to collect, manage, track, and monitor fitness-related activities, such as distance walked, calorie intake, sleep quality, and heart rate. Fitness trackers have become increasingly popular in the past decade. One in five Americans use a device or an app to track their fitness-related activities. These devices generate massive and important data that could help physicians make better assessments of their patients' health if shared with health providers. This ultimately could lead to better health outcomes and perhaps even lower costs for patients. However, sharing personal fitness information with health care providers has drawbacks, mainly related to the risk of privacy loss and information misuse. OBJECTIVE: This study investigates the influence of granting users granular privacy control on their willingness to share fitness information. METHODS: The study used 270 valid responses collected from Mtrurkers through Amazon Mechanical Turk (MTurk). Participants were randomly assigned to one of two groups. The conceptual model was tested using structural equation modeling (SEM). The dependent variable was the intention to share fitness information. The independent variables were perceived risk, perceived benefits, and trust in the system. RESULTS: SEM explained about 60% of the variance in the dependent variable. Three of the four hypotheses were supported. Perceived risk and trust in the system had a significant relationship with the dependent variable, while trust in the system was not significant. CONCLUSIONS: The findings show that people are willing to share their fitness information if they have granular privacy control. This study has practical and theoretical implications. It integrates communication privacy management (CPM) theory with the privacy calculus model.


Asunto(s)
Monitores de Ejercicio , Privacidad , Ejercicio Físico , Humanos , Intención , Confianza
13.
New Bioeth ; 27(4): 320-333, 2021 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-34747348

RESUMEN

The digital revolution has disruptively reshaped the way health services are provided and how research is conducted. This transformation has produced novel ethical challenges. The digitalization of health records, bioinformatics, molecular medicine, wearable biomedical technologies, biotechnology, and synthetic biology has created new biological data niches. How these data are shared, stored, distributed, and analyzed has created ethical problems regarding privacy, trust, accountability, fairness, and justice. This study investigates issues related to data-sharing permissions, fairness in secondary data distribution, and commercial and political conflicts of interest among individuals, companies, and states. In conclusion, establishing an agency to act as deputy trustee on behalf of individuals is recommended to intermediate the complex nature of informed consent. Focusing on decentralized digital technologies is recommended in order to catalyze the utilization of data and prevent discrimination without circulating data unnecessarily.


Asunto(s)
Genómica , Privacidad , Humanos , Difusión de la Información , Consentimiento Informado , Justicia Social
14.
Sensors (Basel) ; 21(21)2021 Nov 02.
Artículo en Inglés | MEDLINE | ID: mdl-34770606

RESUMEN

As a result of the limited resources available in IoT local devices, the large scale cloud consumer's data that are produced by IoT related machines are contracted out to the cloud. Cloud computing is unreliable, using it can compromise user privacy, and data may be leaked. Because cloud-data and grid infrastructure are both growing exponentially, there is an urgent need to explore computational sources and cloud large-data protection. Numerous cloud service categories are assimilated into numerous fields, such as defense systems and pharmaceutical databases, to compute information space and allocation of resources. Attribute Based Encryption (ABE) is a sophisticated approach which can permit employees to specify a higher level of security for data stored in cloud storage facilities. Numerous obsolete ABE techniques are practical when applied to small data sets to generate cryptograms with restricted computational properties; their properties are used to generate the key, encrypt it, and decrypt it. To address the current concerns, a dynamic non-linear polynomial chaotic quantum hash technique on top of secure block chain model can be used for enhancing cloud data security while maintaining user privacy. In the proposed method, customer attributes are guaranteed by using a dynamic non- polynomial chaotic map function for the key initialization, encryption, and decryption. In the proposed model, both organized and unorganized massive clinical data are considered to be inputs for reliable corroboration and encoding. Compared to existing models, the real-time simulation results demonstrate that the stated standard is more precise than 90% in terms of bit change and more precise than 95% in terms of dynamic key generation, encipherment, and decipherment time.


Asunto(s)
Cadena de Bloques , Algoritmos , Nube Computacional , Seguridad Computacional , Privacidad
15.
Stud Health Technol Inform ; 286: 11-15, 2021 Nov 08.
Artículo en Inglés | MEDLINE | ID: mdl-34755682

RESUMEN

Law and regulation have not received much attention as part of the context shaping and being shaped by health informatics. Telemedicine, data, devices and software, and electronic health records (EHRs) are examples of how technologies are affected by privacy, intellectual property protections, and other law and regulation.


Asunto(s)
Informática Médica , Telemedicina , Registros Electrónicos de Salud , Pandemias , Privacidad
16.
Stud Health Technol Inform ; 285: 39-48, 2021 Oct 27.
Artículo en Inglés | MEDLINE | ID: mdl-34734850

RESUMEN

pHealth is a data (personal health information) driven approach that use communication networks and platforms as technical base. Often it' services take place in distributed multi-stakeholder environment. Typical pHealth services for the user are personalized information and recommendations how to manage specific health problems and how to behave healthy (prevention). The rapid development of micro- and nano-sensor technology and signal processing makes it possible for pHealth service provider to collect wide spectrum of personal health related information from vital signs to emotions and health behaviors. This development raises big privacy and trust challenges especially because in pHealth similarly to eCommerce and Internet shopping it is commonly expected that the user automatically trust in service provider and used information systems. Unfortunately, this is a wrong assumption because in pHealth's digital environment it almost impossible for the service user to know to whom to trust, and what the actual level of information privacy is. Therefore, the service user needs tools to evaluate privacy and trust of the service provider and information system used. In this paper, the authors propose a solution for privacy and trust as results of their antecedents, and for the use of computational privacy and trust. To answer the question, which antecedents to use, two literature reviews are performed and 27 privacy and 58 trust attributes suitable for pHealth are found. A proposal how to select a subset of antecedents for real life use is also provided.


Asunto(s)
Registros de Salud Personal , Privacidad , Sistemas de Registros Médicos Computarizados , Confianza
17.
Stud Health Technol Inform ; 285: 253-258, 2021 Oct 27.
Artículo en Inglés | MEDLINE | ID: mdl-34734882

RESUMEN

Genomic information is key for the implementation of real personalized medicine. Nevertheless, access to this kind of information must be controlled because of its high privacy and security requirements. Several genomic information formats exist, although we have started from MPEG-G as it includes metadata and protection mechanisms since its inception and provides a hierarchical structure to organize the information contained. The proposed GIPAMS modular architecture provides a secure and controlled access to genomic information, which may help on improving personalized medicine as described in this paper.


Asunto(s)
Seguridad Computacional , Privacidad , Confidencialidad , Genómica , Sistemas de Información
18.
Comput Intell Neurosci ; 2021: 4244040, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34745246

RESUMEN

Artificial Intelligence has been widely applied today, and the subsequent privacy leakage problems have also been paid attention to. Attacks such as model inference attacks on deep neural networks can easily extract user information from neural networks. Therefore, it is necessary to protect privacy in deep learning. Differential privacy, as a popular topic in privacy-preserving in recent years, which provides rigorous privacy guarantee, can also be used to preserve privacy in deep learning. Although many articles have proposed different methods to combine differential privacy and deep learning, there are no comprehensive papers to analyze and compare the differences and connections between these technologies. For this purpose, this paper is proposed to compare different differential private methods in deep learning. We comparatively analyze and classify several deep learning models under differential privacy. Meanwhile, we also pay attention to the application of differential privacy in Generative Adversarial Networks (GANs), comparing and analyzing these models. Finally, we summarize the application of differential privacy in deep neural networks.


Asunto(s)
Aprendizaje Profundo , Privacidad , Inteligencia Artificial , Redes Neurales de la Computación
19.
Sensors (Basel) ; 21(21)2021 Oct 21.
Artículo en Inglés | MEDLINE | ID: mdl-34770287

RESUMEN

The automotive industry has been transformed through technological progress during the past decade. Vehicles are equipped with multiple computing devices that offer safety, driving assistance, or multimedia services. Despite these advancements, when an incident occurs, such as a car crash, the involved parties often do not take advantage of the technological capabilities of modern vehicles and attempt to assign liability for the incident to a specific vehicle based upon witness statements. In this paper, we propose a secure, decentralized, blockchain-based platform that can be employed to store encrypted position and velocity values for vehicles in a smart city environment. Such data can be decrypted when the need arises, either through the vehicle driver's consent or through the consensus of different authorities. The proposed platform also offers an automated way to resolve disputes between involved parties. A simulation has been conducted upon a mobility traffic dataset for a typical day in the city of Cologne to assess the applicability of the proposed methodology to real-world scenarios and the infrastructure requirements that such an application would have.


Asunto(s)
Conducción de Automóvil , Cadena de Bloques , Simulación por Computador , Privacidad , Tecnología
20.
Sensors (Basel) ; 21(21)2021 Oct 24.
Artículo en Inglés | MEDLINE | ID: mdl-34770352

RESUMEN

Crowdsensing systems have been developed for wide-area sensing tasks because humancarried smartphones are prevailing and becoming capable. To encourage more people to participate in sensing tasks, various incentive mechanisms were proposed. However, participating in sensing tasks and getting rewards can inherently risk the users' privacy and discourage their participation. In particular, the rewarding process can expose the participants' sensor data and possibly link sensitive data to their identities. In this work, we propose a privacy-preserving reward system in crowdsensing using the blind signature. The proposed scheme protects the participants' privacy by decoupling contributions and rewarding claims. Our experiment results show that the proposed mechanism is feasible and efficient.


Asunto(s)
Privacidad , Teléfono Inteligente , Humanos , Recompensa
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...