Privacy Oriented Software Development

Baldassarre, María Teresa; Barletta, Vita Santa; Caivano, Danilo; Scalera, Michele

doi:10.1007/978-3-030-29238-6_2

Cited by 22 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Users modify their online behavior based on the perceived security of the devices they use. In this respect, we agree with Baldassarre et al (2019) who argued that the development of protection and security software should be increased in the future to increase user protection.…”

Section: Discussionsupporting

confidence: 84%

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

Saura

Palacios‐Marqués

Soriano

2023

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

In a digital ecosystem where large amounts of data related to user actions are generated every day, important concerns have emerged about the collection, management, and analysis of these data and, according, about user privacy. In recent years, users have been accustomed to organizing in and relying on digital communities to support and achieve their goals. In this context, the present study aims to identify the main privacy concerns in user communities on social media, and how these affect users’ online behavior. In order to better understand online communities in social networks, privacy concerns, and their connection to user behavior, we developed an innovative and original methodology that combines elements of machine learning as a technical contribution. First, a complex network visualization algorithm known as ForceAtlas2 was used through the open-source software Gephi to visually identify the nodes that form the main communities belonging to the sample of UGC collected from Twitter. Then, a sentiment analysis was applied with Textblob, an algorithm that works with machine learning on which experiments were developed with support vector classifier (SVC), multinomial naïve Bayes (MNB), logistic regression (LR), random forest, and classifier (RFC) under the theoretical frameworks of computer-aided text analysis (CATA) and natural language processing (NLP). As a result, a total of 11 user communities were identified: the positive protection software and cybersecurity and eCommerce, the negative privacy settings, personal information and social engineering, and the neutral privacy concerns, hacking, false information, impersonation and cookies data. The paper concludes with a discussion of the results and their relation to user behavior in digital environments and an outline valuable and practical insights into some techniques and challenges related to users’ personal data.

show abstract

Section: Discussionsupporting

confidence: 84%

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

Saura

Palacios‐Marqués

Soriano

2023

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

show abstract

“…As a result, governments have started to gather and deploy data more effectively [33,34] in order to enhance the development and the sustainability of Smart cities [35], thinking in terms of strategic planning over days, hours, and minutes, rather than years, decades or generations [36].…”

Section: Related Workmentioning

confidence: 99%

Managing a Smart City Integrated Model through Smart Program Management

Barletta¹,

Caivano²,

Dimauro³

et al. 2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Context. A Smart city is intended as a city able to offer advanced integrated services, based on information and communication technology (ICT) technologies and intelligent (smart) use of urban infrastructures for improving the quality of life of its citizens. This goal is pursued by numerous cities worldwide, through smart projects that should contribute to the realization of an integrated vision capable of harmonizing the technologies used and the services developed in various application domains on which a Smart city operates. However, the current scenario is quite different. The projects carried out are independent of each other, often redundant in the services provided, unable to fully exploit the available technologies and reuse the results already obtained in previous projects. Each project is more like a silo than a brick that contributes to the creation of an integrated vision. Therefore, reference models and managerial practices are needed to bring together the efforts in progress towards a shared, integrated, and intelligent vision of a Smart city. Objective. Given these premises, the goal of this research work is to propose a Smart City Integrated Model together with a Smart Program Management approach for managing the interdependencies between project, strategy, and execution, and investigate the potential benefits that derive from using them. Method. Starting from a Smart city worldwide analysis, the Italian scenario was selected, and we carried out a retrospective analysis on a set of 378 projects belonging to nine different Italian Smart cities. Each project was evaluated according to three different perspectives: application domain transversality, technological depth, and interdependences. Results. The results obtained show that the current scenario is far from being considered “smart” and motivates the adoption of a Smart integrated model and Smart program management in the context of a Smart city. Conclusions. The development of a Smart city requires the use of Smart program management, which may significantly improve the level of integration between the application domain transversality and technological depth.

show abstract

“…A cybersecurity solution need not to be limited using a single architecture [32] or a single model [33], and in accordance with that, our research investigates the cybersecurity solutions which propose a distance-based intrusion detection system based on an unsupervised Kohonen SOM network. Therefore, our goal is to test the SOM network in order to identify attacks within the CAN bus.…”

Section: Related Workmentioning

confidence: 85%

Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach

et al. 2020

Self Cite

View full text Add to dashboard Cite

The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach.

show abstract

Privacy Oriented Software Development

Cited by 22 publications

References 20 publications

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

Managing a Smart City Integrated Model through Smart Program Management

Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach

Contact Info

Product

Resources

About