PRIPRO—Privacy Profiles: User Profiling Management for Smart Environments

Cesconetto, Jonas; Silva, Luís Augusto; Bortoluzzi, Fabricio; Navarro-Cáceres, María; Zeferino, Cesar Albenes; Leithardt, Valderi Reis Quietinho

doi:10.3390/electronics9091519

Cited by 18 publications

(11 citation statements)

References 48 publications

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“…Most related works propose preemptive access control [29] and authentication schemes [30] for data mashup privacy preservation in fog computing environments. However, the utility of the published data is lesser in such approaches because they are limited by design to expose publicly available data only.…”

Section: Discussionmentioning

confidence: 99%

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Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

2021

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Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students’ learning. However, data sets involved in mashups may contain personal information of sensitive nature that raises legitimate privacy concerns. Average privacy preservation methods are based on preemptive approaches that limit the published data in a mashup based on access control and authentication schemes. Such limitations may reduce the analytical utility of the data exposed to gain students’ learning insights. In order to reconcile utility and privacy preservation of published data, this research proposes a new data mashup protocol capable of merging and k-anonymizing data sets in cloud-based learning environments without jeopardizing the analytical utility of the information. The implementation of the protocol is based on linked data so that data sets involved in the mashups are semantically described, thereby enabling their combination with relevant educational data sources. The k-anonymized data sets returned by the protocol still retain essential information for supporting general data exploration and statistical analysis tasks. The analytical and empirical evaluation shows that the proposed protocol prevents individuals’ sensitive information from re-identifying.

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Section: Discussionmentioning

confidence: 99%

“…Most works on privacy preservation in distributed data environments propose a preemptive approach, defining who has access to private data attributes and resources by defining user profiles [29]. Access control techniques and authentication-based schemes [30] explicitly grant and revoke data access to parties.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

2021

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“…In the context of time series, there are various scenarios and applications that demand data management and communications between different devices, users and data in general. There are several solutions that address data management based on privacy [14] and information security rules [15], including decentralized implementation [16], smart environments [17,18], disruptive technologies [19], and vulnerability prediction [20].…”

Section: Time Series Forecastingmentioning

confidence: 99%

An Approach to Forecasting Commodity Futures Prices Using Machine Learning

Jesus¹,

Hoppe²,

Sartori³

et al. 2022

Preprint

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This paper presents the development and implementation of a machine learning model to estimate the future price of commodities in the Brazilian market from technical analysis indicators. For this, two databases were obtained regarding the commodities sugar, cotton, corn, soybean and wheat, which were submitted to the steps of data cleaning, pre-processing and subdivision. From the pre-processed data, recurrent neural networks of the long short-term memory type were used to perform the prediction of data in the interval of 1 and 3 days ahead. These models were evaluated using mean squared error, obtaining an accuracy between 0.00010 and 0.00037 on the test data for 1 day ahead and 0.00015 to 0.00041 for 3 days ahead. However, based on the results obtained, it can be stated that the developed model obtained a good prediction performance for all commodities evaluated.

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“…As presented in works [13,30,31], a taxonomy allows the systematic organization of relevant data in the form of a hierarchy. The keywords and concepts used to define a taxonomy establish parameters throughout the information production cycle, in which distributed professionals can participate in the knowledge creation process in an organized way.…”

Section: Taxonomymentioning

confidence: 99%

A Case Study on the Development of a Data Privacy Management Solution Based on Patient Information

Lucca

Silva

Luchtenberg

et al. 2020

Sensors

Self Cite

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Data on diagnosis of infection in the general population are strategic for different applications in the public and private spheres. Among them, the data related to symptoms and people displacement stand out, mainly considering highly contagious diseases. This data is sensitive and requires data privacy initiatives to enable its large-scale use. The search for population-monitoring strategies aims at social tracking, supporting the surveillance of contagions to respond to the confrontation with COVID-19. There are several data privacy issues in environments where IoT devices are used for monitoring hospital processes. In this research, we compare works related to the subject of privacy in the health area. To this end, this research proposes a taxonomy to support the requirements necessary to control patient data privacy in a hospital environment. According to the tests and comparisons made between the variables compared, the application obtained results that contribute to the scenarios applied. In this sense, we modeled and implemented an application. By the end, a mobile application was developed to analyze the privacy and security constraints with COVID-19.

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PRIPRO—Privacy Profiles: User Profiling Management for Smart Environments

Cited by 18 publications

References 48 publications

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

An Approach to Forecasting Commodity Futures Prices Using Machine Learning

A Case Study on the Development of a Data Privacy Management Solution Based on Patient Information

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