The Web of Things (WoT) is a technology concept that allows the integration of the Internet of Things (IoT) with the World Wide Web (WWW). It will vastly affect our lives in the near future given that it offers new services and applications via the well-known web window. In todays’ world where one can hardly imagine everyday life without access to various online services and applications via a plethora of devices, one can notice that technology has a huge impact on our day-to-day quality of living. That is why a user’s Quality of Experience (QoE) towards used technology in general plays a crucial role in their Quality of Life (QoL). Furthermore, security perception in terms of technology is the feature that vastly affects QoE and, consequently, QoL, as the number of security and privacy threats, risks, and vulnerabilities in cyber space, i.e., the technology environment that we increasingly use, is constantly rising. In order to reach the ultimate goals—the adoption of WoT technology and improvement of our QoL—we must know how this important aspect of security is so far addressed and analyzed. Therefore, this paper gives a comprehensive and structured analysis of the existing literature in this field through a proposed framework and provides an overview of research opportunities that should be addressed and elaborated in future investigations.

Users are usually required to share several types of data, including their personal data, as different providers strive to offer high-quality services that are often tailored to end-users’ preferences. However, when it comes to personalizing services, there are several challenges for meeting user’s needs and preferences. For content personalization and delivery of services to end users, services typically create user profiles. When user profiles are created, user data is collected and organized to meet the personalization requirements of the services. In this paper, we provide an overview of current research activities that focus on user profiling and ways to protect user data privacy. The paper presents different types of data that services collect from users on examples of commonly used Internet services. It proposes data categorization as a prerequisite for controlled data sharing between users and Internet services. Furthermore, it discusses how data generalization can be used for anonymization purposes on examples of the proposed data categories. Finally, it gives an overview of the privacy framework being developed and gives guidelines for future work focusing on data generalization methods in order to reduce user privacy risks.

The rapid growth in Internet-based services has increased the demand for user data to enable personalized and adaptive digital experiences. These services typically require users to disclose various types of personal information, which are organized into user profiles and used to tailor content, recommendations, and accessibility settings. However, achieving an effective balance between personalization accuracy and user data protection remains a persistent and complex challenge. Excessive data disclosure raises the risk of re-identification and privacy breaches, while excessive anonymization can significantly diminish personalization and overall service quality. In this paper, we address this trade-off by proposing a context-aware learning-based data generalization framework that preserves user privacy while maintaining the functional usefulness of personal data. We first conduct a systematic classification of user data commonly collected into five main categories: demographic, location, accessibility, preference, and behavior data. To generalize these data categories dynamically and adaptively, we use a Multi-Layer Perceptron (MLP) model that learns patterns across heterogeneous data types. Unlike traditional rule-based generalization techniques, the MLP-based approach captures nonlinear relationships, adapts to heterogeneous data distributions, and scales efficiently with large datasets. The proposed MLP-based generalization method reduces the granularity of personal data, preserving privacy without significantly compromising information usefulness. Experimental results show that the proposed method reduces the risk of re-identification to approximately 35%, compared to non-anonymized data, where the re-identification risk is about 80–90%. These findings highlight the potential of learning-based data generalization as a strategy for privacy-preserving personalization in modern Internet services. They also show how the proposed generalization method can be applied in practice to transform user data while maintaining both utility and confidentiality.