Nowadays, users have access to a wide range of Internet services across different devices. User profiles are crucial for service providers to successfully personalize their content in this competitive market. Therefore, the main goal of personalized services is to collect and analyze users' personal information. The success of these services depends on how well the service provider understands the users and how well this is reflected in the services. In this sense, user profiles are the result of the user profiling process and serve as a representation of the users tailored to the specific service [2].  
Depending on the area of application, the content of the user profile and its scope may change. Regardless of the data, the completeness of the user profile is determined by the methods used to collect and organize the data about the user and how well this data represents the individual. In the literature, two main methods of collecting data about users are distinguished. These methods are referred to as explicit or implicit data collection methods. In explicit methods, users voluntarily provide the system with information about their interests and preferences. In contrast, in the implicit methods, user data are collected dynamically by automatically monitoring the user's interactions with the system, usually through cookies on the websites, APIs, various types of sensors, and so on [2].  
In this paper, we will provide an overview of existing services and the data they collect and process to personalize their services for users. In addition, we will highlight the current state-of-the-art in preserving user privacy in the context of personalizing services and propose guidelines for further work in the area of anonymizing users for personalizing services. In this context, our goal is to determine the extent of similarity of collected data across services and provide a framework for categorizing anonymous user data according to the type of services used.  
As this is a survey paper that aims to categorize user-related data gathered by common Internet services, specific methods for generalizing user data to enhance privacy protection are presented on examples and will be more formally defined and implemented in future work. The main hypothesis of the paper is that services need only specific user data in order to personalize the provided content. By grouping the data, we can define which groups of data the services are allowed to access. Furthermore, the generalization ability enables blurring of user data in order to make it less personally identifiable and, thus, preserving privacy. In this sense, the goal is to categorize user data and offer generalization ability in order to (i) provide only data that services really need and (ii) blur the provided data to an extent that is acceptable to both users and services. The trade-off between personalization and privacy is evident and quite clear because if users want to receive more personalized services they must share more personal data, whereas maintaining higher privacy level, requires'sacrificing' some degree of personalization.  
This paper is organized as follows: After the introduction part, Section 2 presents the most popular Google services and the user data they collect. Section 3 provides an overview of related work by various researchers on privacy and data personalization in user profiling. Section 4 proposes a method for data collection and its classification that includes different types of services. Finally, conclusions and guidelines for future work are given in Section 5.