A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

Wimmer, Hayden; Powell, Loreen

doi:10.14569/ijacsa.2014.051126

Cited by 16 publications

(6 citation statements)

References 42 publications

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“…Privacy-breaching ransomware is characterized by its focus on data theft, distinguishing it from traditional ransomware that primarily seeks to encrypt data for ransom. The literature reveals that this variant poses a more insidious threat, as it not only disrupts operations but also compromises data confidentiality [12]- [16]. Such ransomware typically employs techniques to identify and exfiltrate data of value, often targeting specific file types or databases containing sensitive personal or corporate information [1], [17], [18].…”

Section: A Privacy-breaching Ransomwarementioning

confidence: 99%

Dynamic Behavioural Analysis of Privacy-Breaching and Data Theft Ransomware

Ozturk,

Demir,

Arslan

et al. 2024

Preprint

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This study presents a comprehensive analysis of privacy-breaching ransomware, emphasizing the dynamic behavioral patterns and techniques employed by these malicious entities to compromise data privacy. Employing a systematic methodology that includes the collection of diverse ransomware samples, establishment of a secure analysis environment, and execution of a dynamic behavioral analysis procedure, we have delineated the sophisticated tactics that characterize modern ransomware operations. Our findings reveal a significant shift in ransomware strategies, with an increasing focus on data theft over mere financial extortion, highlighting the evolving landscape of cyber threats. The analysis underscores the necessity for advanced cybersecurity measures that incorporate machine learning and behavioral analysis to proactively detect and mitigate these threats. Furthermore, the study identifies key areas for future research, including the development of adaptive security systems and the importance of international collaboration in cybersecurity efforts. This research contributes valuable insights into the behavior of privacy-breaching ransomware, offering a foundation for future advancements in the field of dynamic ransomware analysis and cybersecurity defense strategies.

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Section: A Privacy-breaching Ransomwarementioning

confidence: 99%

Dynamic Behavioural Analysis of Privacy-Breaching and Data Theft Ransomware

Ozturk,

Demir,

Arslan

et al. 2024

Preprint

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“…The results show that the proposed algorithm achieves a classification accuracy comparable with the benchmark model. Some other work has used standard k-anonymity based privacy models in order to anonymize the training data (Rodríguez-Hoyos et al, 2018), (Herranz et al, 2010), (Wimmer and Powell, 2014). Despite the data distortion the models induced from anonymized data has reported comparable accuracies with the benchmark model.…”

Section: Related Workmentioning

confidence: 99%

Systematic Evaluation of Probabilistic k-Anonymity for Privacy Preserving Micro-data Publishing and Analysis

Senavirathne

Torra

2021

Proceedings of the 18th International Conference on Security and Cryptography

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In the light of stringent privacy laws, data anonymization not only supports privacy preserving data publication (PPDP) but also improves the flexibility of micro-data analysis. Machine learning (ML) is widely used for personal data analysis in the present day thus, it is paramount to understand how to effectively use data anonymization in the ML context. In this work, we introduce an anonymization framework based on the notion of "probabilistic k-anonymity" that can be applied with respect to mixed datasets while addressing the challenges brought forward by the existing syntactic privacy models in the context of ML. Through systematic empirical evaluation, we show that the proposed approach can effectively limit the disclosure risk in micro-data publishing while maintaining a high utility for the ML models induced from the anonymized data.

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“…CHAID algorithm uses Pearson's Chi-square when a target variable is categorical and uses likelihood ratio Chi-square statistic as a separation reference when a target variable is continuous [14]. Chi-square is calculated from the r × c partition …”

Section: B Chi-squared Automatic Interaction Detectionmentioning

confidence: 99%

Chi-Square Automatic Interaction Detection Modeling for Predicting Depression in Multicultural Female Students

Byeon¹

2017

ijacsa

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Abstract-This study developed a depression prediction model for female students from multicultural families by using a decision tree model based on Chi-squared automatic interaction detection (CHAID) algorithm. Subjects of the study were 9,024 female students between 12 and 15 years old among the children of surveyed marriage immigrants. Outcome variables were classified as presence of depression. Explanatory variables included sex, residing area, experience of career counseling, experience of social discrimination, experience of Korean language education, experience of using a multicultural family support center, Korean reading, Korean speaking, Korean writing, Korean listening, Korean society adjustment education experience, needs of Korean society adjustment education, needs of Korean language education, and rejoined entry. In the CHAID algorithm analysis, female students from multicultural families who experienced social discrimination within the past one year and had ordinary Korean speaking skill posed the highest risk of depression. It is necessary to pay social level interests to the mental health of adolescents from multicultural families for achieving successful social integration based on the results of this study.

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A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

Cited by 16 publications

References 42 publications

Dynamic Behavioural Analysis of Privacy-Breaching and Data Theft Ransomware

Dynamic Behavioural Analysis of Privacy-Breaching and Data Theft Ransomware

Systematic Evaluation of Probabilistic k-Anonymity for Privacy Preserving Micro-data Publishing and Analysis

Chi-Square Automatic Interaction Detection Modeling for Predicting Depression in Multicultural Female Students

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