Privacy Threat MOdeling Language

Rodrigues, Andrey; Villela, Maria Luscia; Feitosa, Eduardo

doi:10.1109/access.2023.3255548

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…al. [21] created a Threat modeling methodology for online social networks. The study also evaluates the effectiveness of the methodology on novice users performing Threat modeling.…”

Section: Threat Modeling Articlesmentioning

confidence: 99%

A Systematic Evaluation of Artificial Intelligence's Impact on the Landscape of Threat Modeling

Pai,

R.Kunte,

Najee

2023

STAIQC

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This article systematically evaluates the influence of Artificial Intelligence (AI) on the landscape of Threat modelingin cybersecurity. The study involves an extensive review of relevant journal articles, books, and conference papers to comprehensively assess the current state of the field. By synthesizing existing literature, we identify and analyze the ways in which AItechnologies are applied in Threat modelingmethodologies. The evaluation explores the strengths and limitations of these applications, shedding light on the advancements that have significantly enhancedThreat modeling.Furthermore, the research includes a meticulous gap analysis within the existing literature, revealing areas where further investigation is warranted. Identified gaps in the current research landscape serve as a foundation for proposing future research directions in AI-enhanced Threat modeling. The current literature related to the current landscape of Artificial Intelligence research in cybersecurity predominantly focuses on articles and active studies pertaining to the detection and prevention of cyber-attacks. However, there is a noticeable gap in the existing literature when it comes to leveraging Artificial Intelligence to enhance Threat modeling. While numerous innovative methods have been proposed in recent articles, these predominantly concentrate on Threat modeling within specific domains, such as unmanned vehicles, Cyber-Physical Systems, and Healthcare. There is a lack of generalization in applying these findings to improve Threat modeling practices more broadly. A promising avenue for further research lies in the automation of Threat modeling. Existing literature predominantly emphasizes the study of Threat generation areas, leaving other crucial aspects, such as Architecture representation and Model validation,in need of more comprehensive exploration and analysis

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“…al. [21] created a Threat modeling methodology for online social networks. The study also evaluates the effectiveness of the methodology on novice users performing Threat modeling.…”

Section: Threat Modeling Articlesmentioning

confidence: 99%

A Systematic Evaluation of Artificial Intelligence's Impact on the Landscape of Threat Modeling

Pai,

R.Kunte,

Najee

2023

STAIQC

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“…Inicialmente, dois estudos experimentais foram executados para avaliar a completude, a corretude, a produtividade, a facilidade de uso, utilidade, satisfac ¸ão percebida e intenc ¸ão de uso futuro da PTMOL. Os resultados desses estudos estão disponíveis em [Rodrigues et al 2022, Rodrigues et al 2023b.…”

Section: Validac ¸ãO Empíricaunclassified

“…Diante disso, este trabalho apresenta a PTMOL (Privacy Threat Modeling Language), uma linguagem para a modelagem de ameac ¸as em RSOs, com foco na privacidade do usuário [Rodrigues et al 2023b]. Esta linguagem foi desenvolvida a partir de evidências coletadas na literatura e foi avaliada empiricamente por meio um conjunto de estudos empíricos.…”

Section: Introduc ¸ãOunclassified

Linguagem para a Modelagem de Ameaças de Privacidade

Rodrigues,

Villela,

Feitosa

2023

Anais Estendidos Do XXII Simpósio Brasileiro De Fatores Humanos Em Sistemas Computacionais (IHC 2023)

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Este trabalho apresenta a PTMOL (Privacy Threat Modeling Language), uma linguagem de apoio à modelagem de ameaças de privacidade orientada à Redes Sociais Online (RSOs). A linguagem proposta visa apoiar a busca antecipada por ameaças às quais um usuário poderá estar exposto e quais controles de privacidade uma RSO precisa definir para reduzir os efeitos e consequências dessas ameaças. A linguagem foi avaliada por meio da condução de um conjunto de estudos empíricos que permitiram realizar seus procedimentos de validade e confiabilidade. Os resultados dos estudos indicam que o emprego da linguagem é potencialmente útil para a identificação de ameaças reais de privacidade devido ao caráter exploratório e reflexivo da mesma.

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Privacy-Preserving Techniques for Online Social Networks Data

Belfaik,

Zineddine,

Sadqi

et al. 2024

Risk Assessment and Countermeasures for Cybersecurity

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Online social networks (OSNs) are platforms on the web that have seen significant growth. They allow people to connect, interact, and share information with others over the internet. These platforms have become an essential part of people's daily lives, serving as the primary means of communication for individuals worldwide. However, despite the significant benefits these platforms offer, they face privacy concerns such as identity theft and misuse of users' sensitive information. This chapter aims to provide a comprehensive review of the various privacy weaknesses in OSNs, along with an exploration of the existing mechanisms and techniques used to safeguard users' data within this dynamic environment. The review is based on the rigorous Kitchenham protocol and performs a comprehensive assessment of 26 journal articles, published in the last five years. This study serves as a valuable resource for researchers and developers seeking to enhance the privacy preservation of OSNs.

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Privacy Threat MOdeling Language

Cited by 3 publications

References 51 publications

A Systematic Evaluation of Artificial Intelligence's Impact on the Landscape of Threat Modeling

A Systematic Evaluation of Artificial Intelligence's Impact on the Landscape of Threat Modeling

Linguagem para a Modelagem de Ameaças de Privacidade

Privacy-Preserving Techniques for Online Social Networks Data

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