scholarly journals Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

2021 ◽  
Vol 26 (3) ◽  
pp. 56
Author(s):  
Héber H. Arcolezi ◽  
Selene Cerna ◽  
Christophe Guyeux ◽  
Jean-François Couchot
Keyword(s):  
Response Time ◽  
Rural Areas ◽  
Differential Privacy ◽  
Sensitive Data ◽  
Rescue Service ◽  
Location Data ◽  
Ambulance Dispatch ◽  
Considerable Impact ◽  
The Moment ◽  
Ambulatory Services

Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Abhinav Kumar ◽  
Sanjay Kumar Singh ◽  
K Lakshmanan ◽  
Sonal Saxena ◽  
Sameer Shrivastava
Keyword(s):  
Differential Privacy ◽  
Model Performance ◽  
Healthcare Services ◽  
Cloud Services ◽  
Efficient Computation ◽  
Sensitive Data ◽  
Classification Framework ◽  
Proposed Model ◽  
Diagnostic Framework ◽  
Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

scholarly journals DISRUPCIÓN RURAL: UN PRIMER PASO PARA LA CONSTRUCCIÓN DE DESTINOS TURÍSTICO CON ENFOQUE TERRITORIAL. UNA VISIÓN INTEGRADA DESDE LA “GROUNDED THEORY” / / / \ \ \ RURAL DISRUPTION: A FIRST STEP TOWARDS BUILDING TOURIST DESTINATIONS WITH TERRITORIAL APPROACH. AN INTEGRATED VISION FROM THE GROUNDED THEORY

2020 ◽  
pp. 115
Author(s):  
María del Carmen Solano Báez ◽  
Prudencio José Riquelme Perea ◽  
César García Pina
Keyword(s):  
Grounded Theory ◽  
Rural Areas ◽  
Territorial Development ◽  
Tourist Destinations ◽  
Analysis Methodology ◽  
Inductive Research ◽  
Palabras Clave ◽  
Territorial Approach ◽  
Investigación Cualitativa ◽  
The Moment

Resumen. Esta investigación se enmarca en el estudio del proceso de configuración de destinos turísticos rurales, en el cual se investiga la transición de territorio a destino. El objetivo de este trabajo es analizar el proceso de distanciamiento en el medio rural, identificado gracias a las potencialidades derivadas del uso de la triple codificación que caracteriza la Grounded Theory. El distanciamiento es definido como una desterritorialización multidimensional y caracterizado por cuatro rupturas: socioproductiva, sociocultural, socioambiental y política. Este proceso se identifica como el momento en el cual un territorio oscila entre el declive y la reconstrucción para el impulso del desarrollo territorial basado en las especificidades del territorio. Es una investigación cualitativa inductiva, realizada a partir de la perspectiva glaseriana de la Grounded Theory como metodología de investigación y análisis. Aborda la desterritorialización desde una perspectiva filosófica y económica para explicar el proceso de distanciamiento. Una fase de conceptualización del territorio previa a la construcción de destinos turísticos en el medio rural.   Palabras clave: Grounded Theory, distanciamiento, desterritorialización.   Abstract. This research is part of the study of the process of configuration of rural tourist destinations, in which the transition from territory to destination is researched. The aim of this paper is to analyze the process of distancing in the rural environment, identified thanks to the potentialities derived from the use of the triple coding that characterizes the Grounded Theory. Distancing is defined as a multidimensional deterritorialization, characterized by four ruptures: socio-productive, socio-cultural, socio-environmental and political. This process is identified as the moment in which a territory oscillates between decline and reconstruction in order to promote territorial development based on the specificities of the territory. It is a qualitative inductive research, carried out from the glaserian perspective of the Grounded Theory as a research and analysis methodology. It approaches deterritorialization from a philosophical and economic perspective to explain the process of distancing. A phase of conceptualization of the territory prior to the construction of tourist destinations in the rural areas.   Key words: Grounded Theory, distancing, deterritorialization.

scholarly journals Durable Rural Development Through the 2014 - 2020 National Rural Development Program

2018 ◽  
Vol 13 (2) ◽  
pp. 147-152 ◽  
Author(s):  
Paul Lucian
Keyword(s):  
Rural Development ◽  
Rural Areas ◽  
Rural Economy ◽  
Development Program ◽  
Development Programs ◽  
Local Identity ◽  
Rural Development Program ◽  
Implied Contracts ◽  
The Moment ◽  
The Village

AbstractRural Development Policy is a priority for the E.U., as half of the Union’s population lives in rural areas. This policy is focused on society’s durable development, under all its aspects: economic, social, cultural, and so on. The challenges which rural areas of member states face must be addressed, while at the same time applying European norms and standards for rural development. After Romania became a part of the E.U., rural areas here were supported through several national rural development programs, so as to create a durable and sustainable rural economy. Major changes are required to achieve this kind of development, such as replacing old agricultural structures, modernizing the village, while at the same time maintaining cultural and local identity. Rural areas in Romania are often affected by natural disasters. During the last 17 years, national rural development programs implied contracts worth billions of Euros. For instance, through the 2020 NRDP, a budget of 9.5 billion Euros was allocated, 8.1 billion Euros coming from E.U. funding and 1.34 billion Euros as national cofinancing. At the moment, Romania’s absorption degree for the 2020 NRDP is of 20% and is expected to surpass 50% by 2020. Another regional program includes the concept of Spatial Development - Romania - 2025. Spatial planning supports the avoidance of rural dispersion. The betterment of infrastructure is supported, such as access roads, expanding base utilities, consolidated works to prevent flooding or landslides, and so on.

scholarly journals Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems

2021 ◽  
Author(s):  
Jude TCHAYE-KONDI ◽  
Yanlong Zhai ◽  
Liehuang Zhu
Keyword(s):  
Intelligent Systems ◽  
Differential Privacy ◽  
Inductive Learning ◽  
Random Noise ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Privacy Concerns ◽  
Feature Extractor ◽  
Data Source ◽  
Series Of Experiments

<div>We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and reducing network latency solutions rely on a pre-trained feature extractor deployed on the devices to help extract only important features from the sensitive dataset. However, finding a pre-trained model or pubic dataset to build a feature extractor for certain tasks may turn out to be very challenging. With the large amount of data generated by edge devices, the edge environment does not really lack data, but its improper access may lead to privacy concerns. In this paper, we present DeepGuess , a new privacy-preserving, and latency aware deeplearning framework. DeepGuess uses a new learning mechanism enabled by the AutoEncoder(AE) architecture called Inductive Learning, which makes it possible to train a central neural network using the data produced by end-devices while preserving their privacy. With inductive learning, sensitive data remains on devices and is not explicitly involved in any backpropagation process. The AE’s Encoder is deployed on devices to extracts and transfers important features to the server. To enhance privacy, we propose a new local deferentially private algorithm that allows the Edge devices to apply random noise to features extracted from their sensitive data before transferred to an untrusted server. The experimental evaluation of DeepGuess demonstrates its effectiveness and ability to converge on a series of experiments.</div>

scholarly journals The issues connected with the anonymization of medical data. Part 2. Advanced anonymization and anonymization controlled by owner of protected sensitive data

2014 ◽  
Vol 8 (2) ◽  
pp. 13-24 ◽  
Author(s):  
Arkadiusz Liber
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Data Access ◽  
Medical Data ◽  
Medical Documentation ◽  
Research Review ◽  
Sensitive Data ◽  
Data Anonymization ◽  
Data Access Control ◽  
Anonymized Data

Introduction: Medical documentation ought to be accessible with the preservation of its integrity as well as the protection of personal data. One of the manners of its protection against disclosure is anonymization. Contemporary methods ensure anonymity without the possibility of sensitive data access control. it seems that the future of sensitive data processing systems belongs to the personalized method. In the first part of the paper k-Anonymity, (X,y)- Anonymity, (α,k)- Anonymity, and (k,e)-Anonymity methods were discussed. these methods belong to well - known elementary methods which are the subject of a significant number of publications. As the source papers to this part, Samarati, Sweeney, wang, wong and zhang’s works were accredited. the selection of these publications is justified by their wider research review work led, for instance, by Fung, Wang, Fu and y. however, it should be noted that the methods of anonymization derive from the methods of statistical databases protection from the 70s of 20th century. Due to the interrelated content and literature references the first and the second part of this article constitute the integral whole.Aim of the study: The analysis of the methods of anonymization, the analysis of the methods of protection of anonymized data, the study of a new security type of privacy enabling device to control disclosing sensitive data by the entity which this data concerns.Material and methods: Analytical methods, algebraic methods.Results: Delivering material supporting the choice and analysis of the ways of anonymization of medical data, developing a new privacy protection solution enabling the control of sensitive data by entities which this data concerns.Conclusions: In the paper the analysis of solutions for data anonymization, to ensure privacy protection in medical data sets, was conducted. the methods of: k-Anonymity, (X,y)- Anonymity, (α,k)- Anonymity, (k,e)-Anonymity, (X,y)-Privacy, lKc-Privacy, l-Diversity, (X,y)-linkability, t-closeness, confidence Bounding and Personalized Privacy were described, explained and analyzed. The analysis of solutions of controlling sensitive data by their owner was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the protection of anonymized data was included. In particular, the methods of: δ-Presence, e-Differential Privacy, (d,γ)-Privacy, (α,β)-Distributing Privacy and protections against (c,t)-isolation were analyzed. Moreover, the author introduced a new solution of the controlled protection of privacy. the solution is based on marking a protected field and the multi-key encryption of sensitive value. The suggested way of marking the fields is in accordance with Xmlstandard. For the encryption, (n,p) different keys cipher was selected. to decipher the content the p keys of n were used. The proposed solution enables to apply brand new methods to control privacy of disclosing sensitive data.

scholarly journals Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

2019 ◽  
Vol 1 (1) ◽  
pp. 483-491 ◽  
Author(s):  
Makhamisa Senekane
Keyword(s):  
Support Vector Machine ◽  
Data Analysis ◽  
Image Classification ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Global Optimum ◽  
Support Vector ◽  
Sensitive Data ◽  
Radiological Images ◽  
Golden Standard

The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.

Geographic Identity and Attitudes toward Undocumented Immigrants

2019 ◽  
Vol 72 (4) ◽  
pp. 944-959 ◽  
Author(s):  
Lorrie Frasure-Yokley ◽  
Bryan Wilcox-Archuleta
Keyword(s):  
United States ◽  
Public Opinion ◽  
Rural Areas ◽  
Undocumented Immigrants ◽  
The United States ◽  
Neighborhood Context ◽  
Attitudes Toward Immigrants ◽  
Explanatory Factors ◽  
Considerable Impact ◽  
Socio Demographic Factors

This article examines the extent to which economic attitudes, political predispositions, neighborhood context, and socio-demographic factors influence views toward adult, undocumented immigrants living and working in the United States. We specifically examine how these factors differ for respondents living in various types of American urban, suburban, and rural areas. Arguably, in the aftermath of the 2016 Presidential election, public opinion toward often racialized immigration policy proposals is incomplete without an understanding of the role of place and geographic identity. In the 2016 general election, 62 percent of rural voters cast a ballot for Trump, as compared with 50 percent of suburban voters, and 35 percent of urban voters. However, we know little about how their views toward undocumented immigration, a persistent hot-button issue, varied by geographic type. Our findings suggest that views toward undocumented immigrants currently living and working in the United States are conditioned by factors related to a respondent’s geographic type. We find that attitudes toward immigrants vary considerably across place. These findings provide support to our argument about the development of a geographic-based identity that has considerable impact on important public opinion attitudes, even after controlling for more traditional explanatory factors.

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