A systematic overview on methods to protect sensitive data provided for various analyses

Templ, Matthias; Sariyar, Murat

doi:10.1007/s10207-022-00607-5

Cited by 5 publications

(3 citation statements)

References 57 publications

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“…Comprehending and protecting sensitive information is the other important training, the information needs to be protected to prevent that data from being misused by third parties for fraud, such as phishing scams and identity theft [38]. Data protection is also essential to help control cybercrimes by ensuring details (specifically banking) and reference report are covered to prevent deception.…”

Section: Resultsmentioning

confidence: 99%

Socio Technical Framework to Improve Work Behavior During Smart City Implementation

Haryadi¹,

Karim²,

Salahuddin³

2023

IJACSA

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Every organization uniquely adheres to security culture. Numerous studies have discovered that procrastinating, impulsive, forward-thinking, and risk-taking behaviors vary across organizations, which may help to explain why different organizations' adherence to security policies. This study describes the human aspect of a government organization in contributing to the successful implementation of a smart city by minimizing cybersecurity threats. Improper employee behavior and lack of understanding of cybersecurity will negatively contribute to the successful development of smart cities. The purpose of this research is to develop a framework to determine the factors to improve work behavior in terms of the contribution of social and technical factors. The use of a socio-technical approach to explain how socio-technical integration can contribute to improving work behavior by using mixed methods. The results indicated that several socio-technical factors which include technology, IT infrastructure, work organization, competency, training, and teamwork contribute to improving work behaviors which can be used as a basis for minimizing cybersecurity threats in smart city implementation.

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Section: Resultsmentioning

confidence: 99%

Socio Technical Framework to Improve Work Behavior During Smart City Implementation

Haryadi¹,

Karim²,

Salahuddin³

2023

IJACSA

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“…Technical safeguards (e.g. anonymization [ 19 ] and encryption [ 20 ]) need to be implemented to ensure data security and data protection [ 21 ]. In addition, because sensitive data are often stored in different formats, reflecting different source systems, prior data harmonization may be required to support sharing and integration of those data [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Sharing sensitive data in life sciences: an overview of centralized and federated approaches

Rujano,

Boiten,

Ohmann

et al. 2024

Briefings in Bioinformatics

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Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator’s premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

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“…This sensitive information may be stolen or leaked by malware that is included in PDF files, which might result in monetary loss, data breaches, or identity theft. Protecting the security and integrity of sensitive data is made easier by finding and eliminating malware from PDF files [8].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Models for PDF Malware Detection: Evaluating Different Training and Testing Criteria

Khan,

Arshad,

Shah Khan

2023

Journal of Cyber Security

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The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks. Portable Document Format (PDF) files have emerged as a major attack vector for malware due to their adaptability and wide usage. Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts, exploits, and malicious URLs. This paper presents a comparative analysis of machine learning (ML) techniques, including Naive Bayes (NB), K-Nearest Neighbor (KNN), Average One Dependency Estimator (A1DE), Random Forest (RF), and Support Vector Machine (SVM) for PDF malware detection. The study utilizes a dataset obtained from the Canadian Institute for Cyber-security and employs different testing criteria, namely percentage splitting and 10-fold cross-validation. The performance of the techniques is evaluated using F1score, precision, recall, and accuracy measures. The results indicate that KNN outperforms other models, achieving an accuracy of 99.8599% using 10-fold cross-validation. The findings highlight the effectiveness of ML models in accurately detecting PDF malware and provide insights for developing robust systems to protect against malicious activities. KEYWORDSCyber-security; PDF malware; model training; testing to the research team members, Bilal Khan (BK), Muhammad Arshad (MA), and Sarwar Shah Khan (SSK), for their collaboration and valuable insights throughout the research process. We extend our appreciation to the institutions that supported this research work. We are deeply grateful to the Canadian Institute for Cyber-security for providing the dataset that formed the foundation of our analysis. Their contributions have been instrumental in enabling us to conduct this study on PDF malware detection and assess the performance of various ML models. Our heartfelt appreciation goes to all the individuals and institutions that reviewed and provided constructive feedback on this research paper. Your valuable input helped improve the quality and rigor of our work. Without the collective efforts and support of all these individuals and organizations, this research paper would not have been possible. Thank you all for your invaluable contributions.

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A systematic overview on methods to protect sensitive data provided for various analyses

Cited by 5 publications

References 57 publications

Socio Technical Framework to Improve Work Behavior During Smart City Implementation

Socio Technical Framework to Improve Work Behavior During Smart City Implementation

Sharing sensitive data in life sciences: an overview of centralized and federated approaches

Comparative Analysis of Machine Learning Models for PDF Malware Detection: Evaluating Different Training and Testing Criteria

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