scholarly journals Efficient Data Privacy and Security in Autonomous Cars

2019 ◽  
Vol 7 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Rushit Dave ◽  
Evelyn R Sowells Boone ◽  
Kaushik Roy
Keyword(s):  
Data Privacy ◽  
Privacy And Security ◽  
Autonomous Cars ◽  
Efficient Data

Data-driven sector coupling in 5G-based smart networks

2021 ◽  
Vol 22 (1) ◽  
pp. 53-68
Author(s):  
Guenter Knieps
Keyword(s):  
Data Privacy ◽  
Service Providers ◽  
Policy Implications ◽  
Network Industries ◽  
5G Networks ◽  
Privacy And Security ◽  
Open Set ◽  
Application Service ◽  
Downstream Application

5G attains the role of a GPT for an open set of downstream IoT applications in various network industries and within the app economy more generally. Traditionally, sector coupling has been a rather narrow concept focusing on the horizontal synergies of urban system integration in terms of transport, energy, and waste systems, or else the creation of new intermodal markets. The transition toward 5G has fundamentally changed the framing of sector coupling in network industries by underscoring the relevance of differentiating between horizontal and vertical sector coupling. Due to the fixed mobile convergence and the large open set of complementary use cases, 5G has taken on the characteristics of a generalized purpose technology (GPT) in its role as the enabler of a large variety of smart network applications. Due to this vertical relationship, characterized by pervasiveness and innovational complementarities between upstream 5G networks and downstream application sectors, vertical sector coupling between the provider of an upstream GPT and different downstream application industries has acquired particular relevance. In contrast to horizontal sector coupling among different application sectors, the driver of vertical sector coupling is that each of the heterogeneous application sectors requires a critical input from the upstream 5G network provider and combines this with its own downstream technology. Of particular relevance for vertical sector coupling are the innovational complementarities between upstream GPT and downstream application sectors. The focus on vertical sector coupling also has important policy implications. Although the evolution of 5G networks strongly depends on the entrepreneurial, market-driven activities of broadband network operators and application service providers, the future of 5G as a GPT is heavily contingent on the role of frequency management authorities and European regulatory policy with regard to data privacy and security regulations.

scholarly journals A Cloud Game-Based Educative Platform Architecture: The CyberScratch Project

2021 ◽  
Vol 11 (2) ◽  
pp. 807
Author(s):  
Llanos Tobarra ◽  
Alejandro Utrilla ◽  
Antonio Robles-Gómez ◽  
Rafael Pastor-Vargas ◽  
Roberto Hernández
Keyword(s):  
Data Storage ◽  
Data Privacy ◽  
Data Science ◽  
Critical Issue ◽  
Privacy Management ◽  
Instructional Process ◽  
Legal Context ◽  
Flexible Architecture ◽  
Efficient Data ◽  
Implementation Guidelines

The employment of modern technologies is widespread in our society, so the inclusion of practical activities for education has become essential and useful at the same time. These activities are more noticeable in Engineering, in areas such as cybersecurity, data science, artificial intelligence, etc. Additionally, these activities acquire even more relevance with a distance education methodology, as our case is. The inclusion of these practical activities has clear advantages, such as (1) promoting critical thinking and (2) improving students’ abilities and skills for their professional careers. There are several options, such as the use of remote and virtual laboratories, virtual reality and game-based platforms, among others. This work addresses the development of a new cloud game-based educational platform, which defines a modular and flexible architecture (using light containers). This architecture provides interactive and monitoring services and data storage in a transparent way. The platform uses gamification to integrate the game as part of the instructional process. The CyberScratch project is a particular implementation of this architecture focused on cybersecurity game-based activities. The data privacy management is a critical issue for these kinds of platforms, so the architecture is designed with this feature integrated in the platform components. To achieve this goal, we first focus on all the privacy aspects for the data generated by our cloud game-based platform, by considering the European legal context for data privacy following GDPR and ISO/IEC TR 20748-1:2016 recommendations for Learning Analytics (LA). Our second objective is to provide implementation guidelines for efficient data privacy management for our cloud game-based educative platform. All these contributions are not found in current related works. The CyberScratch project, which was approved by UNED for the year 2020, considers using the xAPI standard for data handling and services for the game editor, game engine and game monitor modules of CyberScratch. Therefore, apart from considering GDPR privacy and LA recommendations, our cloud game-based architecture covers all phases from game creation to the final users’ interactions with the game.

scholarly journals Towards Better Performance for Protected Iris Biometric System with Confidence Matrix

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 910
Author(s):  
Tong-Yuen Chai ◽  
Bok-Min Goi ◽  
Wun-She Yap
Keyword(s):  
Data Privacy ◽  
Recognition Performance ◽  
Generalized Solution ◽  
Performance Degradation ◽  
Public Confidence ◽  
Data Types ◽  
Privacy And Security ◽  
Biometric Data ◽  
Biometric Template ◽  
Template Protection

Biometric template protection (BTP) schemes are implemented to increase public confidence in biometric systems regarding data privacy and security in recent years. The introduction of BTP has naturally incurred loss of information for security, which leads to performance degradation at the matching stage. Although efforts are shown in the extended work of some iris BTP schemes to improve their recognition performance, there is still a lack of a generalized solution for this problem. In this paper, a trainable approach that requires no further modification on the protected iris biometric templates has been proposed. This approach consists of two strategies to generate a confidence matrix to reduce the performance degradation of iris BTP schemes. The proposed binary confidence matrix showed better performance in noisy iris data, whereas the probability confidence matrix showed better performance in iris databases with better image quality. In addition, our proposed scheme has also taken into consideration the potential effects in recognition performance, which are caused by the database-associated noise masks and the variation in biometric data types produced by different iris BTP schemes. The proposed scheme has reported remarkable improvement in our experiments with various publicly available iris research databases being tested.

Inconsistencies in Overdose Suicide Death Investigation Practice and Potential Remedies Using Technology: A Centers for Disease Control and Prevention Consultation Meeting Summary

2021 ◽  
pp. 192536212110224
Author(s):  
Melissa C. Mercado ◽  
Deborah M. Stone ◽  
Caroline W. Kokubun ◽  
Aimée-Rika T. Trudeau ◽  
Elizabeth Gaylor ◽  
...  
Keyword(s):  
System Integration ◽  
Data Privacy ◽  
Burden Of Proof ◽  
The United States ◽  
Privacy And Security ◽  
Death Scene ◽  
Undetermined Intent ◽  
Control And Prevention ◽  
Scene Description ◽  
Death Investigation

Introduction: It is widely accepted that suicides—which account for more than 47 500 deaths per year in the United States—are undercounted by 10% to 30%, partially due to incomplete death scene investigations (DSI) and varying burden-of-proof standards across jurisdictions. This may result in the misclassification of overdose-related suicides as accidents or undetermined intent. Methods: Virtual and in-person meetings were held with suicidologists and DSI experts from five states (Spring-Summer 2017) to explore how features of a hypothetical electronic DSI tool may help address these challenges. Results: Participants envisioned a mobile DSI application for cell phones, tablets, or laptop computers. Features for systematic information collection, scene description, and guiding key informant interviews were perceived as useful for less-experienced investigators. Discussion: Wide adoption may be challenging due to differences in DSI standards, practices, costs, data privacy and security, and system integration needs. However, technological tools that support consistent and complete DSIs could strengthen the information needed to accurately identify overdose suicides.

scholarly journals Secure Framework Enhancing AES Algorithm in Cloud Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ijaz Ahmad Awan ◽  
Muhammad Shiraz ◽  
Muhammad Usman Hashmi ◽  
Qaisar Shaheen ◽  
Rizwan Akhtar ◽  
...  
Keyword(s):  
Data Privacy ◽  
Security Threats ◽  
Privacy And Security ◽  
Plain Text ◽  
Aes Algorithm ◽  
Confidential Data ◽  
Network Usage ◽  
Data Centres ◽  
Demand Service ◽  
Remote Data

The tremendous growth of computational clouds has attracted and enabled intensive computation on resource-constrained client devices. Predominantly, smart mobiles are enabled to deploy data and computational intensive applications by leveraging on the demand service model of remote data centres. However, outsourcing personal and confidential data to the remote data servers is challenging for the reason of new issues involved in data privacy and security. Therefore, the traditional advanced encryption standard (AES) algorithm needs to be enhanced in order to cope with the emerging security threats in the cloud environment. This research presents a framework with key features including enhanced security and owner’s data privacy. It modifies the 128 AES algorithm to increase the speed of the encryption process, 1000 blocks per second, by the double round key feature. However, traditionally, there is a single round key with 800 blocks per second. The proposed algorithm involves less power consumption, better load balancing, and enhanced trust and resource management on the network. The proposed framework includes deployment of AES with 16, 32, 64, and 128 plain text bytes. Simulation results are visualized in a way that depicts suitability of the algorithm while achieving particular quality attributes. Results show that the proposed framework minimizes energy consumption by 14.43%, network usage by 11.53%, and delay by 15.67%. Hence, the proposed framework enhances security, minimizes resource utilization, and reduces delay while deploying services of computational clouds.

scholarly journals Solutions to Big Data Privacy and Security Challenges Associated With COVID-19 Surveillance Systems

2021 ◽  
Vol 4 ◽  
Author(s):  
Vibhushinie Bentotahewa ◽  
Chaminda Hewage ◽  
Jason Williams
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Personal Data ◽  
Surveillance Systems ◽  
Privacy Rights ◽  
Personal Privacy ◽  
Privacy And Security ◽  
Digital Surveillance ◽  
Feasible Solutions ◽  
The Impact

The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.

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