Malware targeting user privacy has seen a surge in recent times, attributed to evolving global regulations and the boost of electronic commerce and online services. Moreover, recognizing privacy malware that employs obfuscation as evasion mechanism presents a major challenge due to its dynamics, resilience, and polymorphism at runtime, necessitating the application of forensic techniques such as memory dumping analysis in order to reveal suitable patterns and behaviors that enable its subsequent detection and classification. In this paper, we present three obfuscated privacy malware classifiers trained on the CIC-MalMem-2022 dataset. These solutions include a binary classifier to distinguish benign from malicious samples using logistic regression (LR), a multiclass classifier that further categorizes benign, spyware, ransomware, and trojan horse obfuscated privacy malware; and a more detailed multiclass classifier capable of discriminating benign samples from fifteen specific obfuscated privacy malware families. Multiclass classifiers were built using several traditional machine learning algorithms and a novel Deep Neural Network (DNN). We applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalances. In particular, our results demonstrate that DNN outperforms traditional machine learning algorithms, yielding statistically significant improvements in key metrics. These achievements reach practical thresholds, suggesting the potential for enhanced malware protection systems. The dataset and all the coding files required for experiments reproducibility are publicly available at https://github.com/dcevallossalas/PrivacyMalwareClassifiers.

While Internet privacy is a subjective term that is challenging to define, describe, and quantify, assessing the level of privacy provided by data processors offering services over the Internet is essential for detecting privacy flaws and enabling continuous improvement. Moreover, assessing Internet privacy is fundamental for estimating the risk of personal data disclosure, the degree of compliance with privacy regulations, and the effectiveness of implemented protection mechanisms. Remarkably, the absence of a standardized criterion for this assessment has led to the proliferation of diverse heuristic techniques applied with different approaches. In this paper, we conduct an in-depth analysis and introduce a novel taxonomy for categorizing existing heuristic techniques to assess Internet privacy. Moreover, we scrutinize various protection mechanisms designed to enhance users’ privacy. We cover this broad topic across all domains of application and levels of automation, considering all relevant papers regardless of publication year, ultimately providing a comprehensive review of this important field of knowledge. Leveraging our proposed classification framework, we systematically organize and categorize 160 papers carefully selected from 934 candidates, elucidating existing gaps and challenges while foreseeing future research directions. Overall, our findings reveal that most studies predominantly rely on information measurement methods for assessing Internet privacy. Although most heuristic techniques are based on automatic mechanisms, they are applied with a clear focus on the traditional use of Internet services through a web browser, demanding more research efforts for other domains. The development of new technologies that incorporate privacy-by-default and include telemetry modules in their architectures will be essential for assessing and enhancing users’ privacy when delivering services over the future Internet.

E-government and transparency are significantly improving public service management by encouraging trust, accountability, and the massive participation of citizens. On the one hand, e-government has facilitated online services to address bureaucratic processes more efficiently. On the other hand, transparency has promoted open access to public information from the State so that citizens can understand and track aspects of government processes more effectively. However, as both require extensive citizen information management, these initiatives may significantly compromise privacy by exposing personal data. To assess these privacy risks in a concrete scenario, we analyzed 21 public institutions in Ecuador through a proposed taxonomy of 6 categories and 17 subcategories of disclosed personal data on their online portals and websites due to LOTAIP transparency initiative. Moreover, 64 open-access systems from these 21 public institutions that accomplish e-government principles were analyzed through a proposed taxonomy of 8 categories and 77 subcategories of disclosed personal data. Our results suggest that personal data are not handled through suitable protection mechanisms, making them extremely vulnerable to manual and automated exfiltration attacks. The lack of awareness campaigns in Ecuador has also led many citizens to handle their personal data carelessly without being aware of the associated risks. Moreover, Ecuadorian citizens’ privacy is significantly compromised, including personal data from children and teenagers being intentionally exposed through e-government and transparency initiatives.

One of the main disadvantages of symmetric key algorithms in compar-ison with asymmetric key ones is their lower security level due to theirshorter key length. Indeed, increasing the number of bits that conformsthe key used by a symmetric cryptography algorithm will increase its se-curity level with a cost on its performance. Expanding a key in symmetriccryptography is not an easy task due to algorithms are designed to workwith keys of a fixed maximum length. This paper presents an alterna-tive design of the RC5 cryptography algorithm with an enhanced securitylevel achieved through a key expansion mechanism with Parallel Comput-ing processing. Although the design was created for the RC5 algorithmthe main idea might be applied to other block cipher algorithms applyingthe same criteria. This methodology makes feasible to obtain a robustsymmetric key algorithm implemented in software with an acceptable per-formance in comparison with other techniques such as implementations inhardware, reduction in the amount of data, improvements in the key ex-change process, advanced high performance computing, and many otherstechniques