## 4 Discussion and Proposed Privacy Model Based on Service Category  
As users of today's technologies, we are amazed with many features that these technologies can provide. However, users often do not consider the implications with respect to privacy issues and sharing vast amounts of personal data, leading to several security threats and data breaches. These problems are more complex given the fact that users sometimes may compromise privacy and security in order to obtain access to the latest technologies and services [44]. With respect to this issue, numerous architectures are designed with the aim to overcome technical challenges related to delivering different kinds of services in the future Internet [45]. Generally speaking, security and privacy issues arise especially for clients who use intelligent end devices. Table 1 indicates sensitive data that can be stored on both the client side and in the operator network, such as history records, application usage, user mobility, contacts, location, etc. Therefore, this collected data has the potential to facilitate different services to personalize those kinds of data. Due to this fact, an appropriate level of security must be guaranteed, and a proper balance should be made between the level of security and performance as well. Delivering \"personalized\" services to end users has a few challenges, whereby users may not be so keen on sharing certain personal data [46]. On the other hand, it should be taken into account that, nowadays, users generally tend to \"accept all terms and conditions\", for example, when using Google services and, thus, allowing Google access to a large number of user-related data [47]. Our proposal is to define a privacy framework as a central node that would be responsible for user data aggregation, handling, and sharing across services. When a user accesses a service that requires access to his/her data, the service would request certain data category or multiple categories, as defined in Table 3. In services nowadays, this is being performed directly through forms or implicit data acquisition, but in our proposal the idea is that the service would ask these data from the proposed privacy framework, in a similar manner to how it is being carried out in Single Sign On (SSO) [48] using the OAuth protocol. The privacy framework would then control users' dynamic consent and real-time privacy adjustments based on the current user input, where users can approve or reject one-time data sharing. The framework also provides information about the types of data that the service gathers from users and informs users about potential risks if the required data is leaked. An important aspect of our proposal is that users only give their data when using the service, which means that their data is shared on a per-session basis, rather than in its entirety and, thus, it is not being stored long-term in the services domain and, thus, minimizes the risks of data leaks from services handling the data. Our approach focuses on taking the data collected by services, estimating the associated risks, and then categorizing them for category-based sharing and anonymization purposes. Anonymization can be performed by generalization of the acquired data. For example, some services might provide well-personalized content even with generalized data (e.g., weather forecast), while some cannot (e.g., online deliveries from web shops). In our proposal, it is up to the service to define the minimum amount of necessary data and the granularity of the data, and it is up to a user to decide how much is he/she willing to disclose in order to get a more personalized service. One example of how anonymization can be performed is by generalizing location data, as it is presented by authors in [49]. They proposed a location proxy to preserve user privacy by limiting the use of precise location data. The idea of the proxy is to somehow \"blur\" more precise user locations where such precision is unnecessary.