Arielle Lucca

Ms. Arielle Lucca

Universidade do Vale do Itajaí

Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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Journal Article
Open access
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Arielle LuccaArielle Lucca
Arielle Lucca
GSGuilherme Augusto Mariano Sborz
Guilherme Augusto Mariano Sborz
Valderi Reis Quietinho LeithardtValderi Reis Quietinho Leithardt
Valderi Reis Quietinho Leithardt
Marko BekoMarko Beko
Marko Beko
Cesar ZeferinoCesar Zeferino
Cesar Zeferino
Wemerson Delcio ParreiraWemerson Delcio Parreira
Wemerson Delcio Parreira
Cryptography is considered indispensable among security measures applied to data concerning insecure means of transmission. Among various existent algorithms on asymmetric cryptography, we may cite Elliptic Curve Cryptography (ECC), which has been widely used due to its security level and reduced key sizes. When compared to Rivest, Shamir and Adleman (RSA), for example, ECC can maintain security levels with a shorter key. Elliptic Curve Point Multiplication (ECPM) is the main function in ECC, and is the component with the highest hardware cost. Lots of ECPM implementations have been applied on hardware targeting the acceleration of its calculus. This article presents a systematic review of literature on ECPM implementations on both Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC). The obtained results show which methods and technologies have been used to implement ECPM on hardware and present some findings of the choices available to the hardware designers.

23 October 2020 in Sensors
Verified
Journal Article
Open access
PDF file
Arielle LuccaArielle Lucca
Arielle Lucca
Luís Augusto SilvaLuís Augusto Silva
Luís Augusto Silva
RLRodrigo Luchtenberg
Rodrigo Luchtenberg
LGLeonardo Garcez
Leonardo Garcez
XMXuzeng Mao
Xuzeng Mao
Valderi Reis Quietinho LeithardtValderi Reis Quietinho Leithardt
Valderi Reis Quietinho Leithardt
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 Coronavirus 2 (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.