Security and privacy risk estimation for personal data stored on mobile devices aposteriori statistical approach to risk estimation

2017 ◽  
Author(s):  
Mikhalsky Oleg ◽  
Pshehotskaya Ekaterina
Keyword(s):  
Mobile Devices ◽  
Statistical Approach ◽  
Risk Estimation ◽  
Personal Data ◽  
Security And Privacy ◽  
Privacy Risk

scholarly journals Using the Same PayWord Chains Associated with a Single Account from Multiple Mobile Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Tao-Ku Chang ◽  
Fu-Hao Yeh
Keyword(s):  
Mobile Devices ◽  
Personal Data ◽  
Security And Privacy ◽  
Mobile Payments ◽  
Multiple Devices ◽  
Privacy Risks

Customer awareness and interest in mobile payments are increasing. However, security and privacy risks remain major barriers to their adoption, with customers worrying about their personal data being hacked or intercepted. In this paper, we present the design of a secure scheme for mobile payments that can guarantee mutual nonrepudiation between the customer, merchant, and banker. A customer can use the proposed scheme to make a payment with the same PayWord chains of a single account from multiple devices.

Behavioral Economics Issues for Software Requirements Optimization for Personal Data Security and Privacy

2021 ◽  
Author(s):  
Shatadru Shikta ◽  
Somania Nur Mahal ◽  
Kazi Bushra Al Jannat ◽  
MAHADY HASAN ◽  
M. ROKONUZZAMAN
Keyword(s):  
Behavioral Economics ◽  
Data Security ◽  
Personal Data ◽  
Security And Privacy ◽  
Software Requirements

scholarly journals Estimation of the return period of rockfalls according to the block size

2016 ◽  
Author(s):  
Valerio De Biagi ◽  
Maria Lia Napoli ◽  
Monica Barbero ◽  
Daniele Peila
Keyword(s):  
Return Period ◽  
Statistical Approach ◽  
Historical Data ◽  
Risk Estimation ◽  
Block Size ◽  
Return Time ◽  
Correct Definition ◽  
Protection Devices ◽  
Definition Of ◽  
Rockfall Protection

Abstract. With reference to the rockfall risk estimation and the planning of rockfall protection devices one of the most critical and most discussed problems is the correct definition of the design block taking into account its return period. In this paper, a methodology for the assessment of the design block linked with its return time is proposed and discussed, following a statistical approach. The procedure is based on the survey of the blocks already detached from the slope and accumulated at the foot of the slope and the available historical data.

scholarly journals Is Big Data Security Essential for Students to Understand?

2020 ◽  
Vol 11 (2) ◽  
pp. 161-170
Author(s):  
Rochman Hadi Mustofa
Keyword(s):  
Big Data ◽  
Undergraduate Students ◽  
Data Security ◽  
Personal Data ◽  
Digital Data ◽  
Security And Privacy ◽  
Group Discussions ◽  
Technology Applications ◽  
Digital Payments ◽  
Financial Technology

AbstractBig Data has become a significant concern of the world, along with the era of digital transformation. However, there are still many young people, especially in developing countries, who are not yet aware of the security of their big data, especially personal data. Misuse of information from big data often results in violations of privacy, security, and cybercrime. This study aims to determine how aware of the younger generation of security and privacy of their big data. Data were collected qualitatively by interviews and focus group discussions (FGD) from. Respondents were undergraduate students who used social media and financial technology applications such as online shopping, digital payments, digital wallet and hotel/transportation booking applications. The results showed that students were not aware enough and understood the security or privacy of their digital data, and some respondents even gave personal data to potentially scam sites. Most students are not careful in providing big data information because they are not aware of the risks behind it, socialization is needed in the future as a step to prevent potential data theft.

scholarly journals Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiru Li ◽  
Wei Xu ◽  
Huibin Shi ◽  
Yuanyuan Zhang ◽  
Yan Yan
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Risk Assessment ◽  
Big Data ◽  
Data Storage ◽  
Bp Neural Network ◽  
Security And Privacy ◽  
Cloud Environment ◽  
Privacy Risk ◽  
Whole Life Cycle

Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.

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