# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 2 Overview of the FRS
### Categories of FRS  
In this section, we explore the different categories of FRSs and discuss how FRSs can be used to improve the accuracy and efficiency of recommendations. We also present how these categories can pose serious privacy concerns to users' sensitive data. In Figure 4, we present the categories of FRSs. In Table 1, we briefly highlight the pros and cons of each below mentioned FRS technique. * **Content-based filtering**: This type of FRS uses content-based information to provide personalized recommendations to users without needing to collect and store personal user data [45; 46]. FRSs work by aggregating data from multiple sources, such as online stores, websites, and other applications, and using algorithms to detect patterns in the data. To this end, content-based filtering uses information about the content of the recommended items, such as genre, author, or keywords, to generate recom  
Figure 4: Categories of federated recommendation systems. mendations [47]. Content-based filtering has several advantages over other forms of FRSs. It does not require any user data, so it can protect user privacy by avoiding the need to collect and store any sensitive personal information. Additionally, it is less resource-intensive than other forms of FRSs, since it does not require creating a centralized database to store user data. Moreover, it is easy to implement since it does not require a complex algorithm to generate the recommendations [48]. Content-based filtering is often used in conjunction with other forms of FRSs, such as collaborative filtering, in order to generate more accurate and personalized recommendations [49]. This category is becoming increasingly popular among online retailers and other businesses due to its effectiveness and privacy-preserving benefits. However, content-based filtering requires access to the user's interaction data to make recommendations, which means that the system can access the user's preferences and behavior, which could be sensitive information. Therefore, without proper privacy measures in place, content-based filtering systems can pose a risk to the user's privacy. * **Knowledge-based systems**: This type of system uses a \"knowledge base\" to create recommendations based on user preferences and behavior, rather than relying solely on data collected from an individual user [50; 51]. This means that the system does not collect any data from the user, instead relying on a pre-existing collection of information. The knowledge base is typically a collection of data about different products, services, or experiences [52]. It contains information about the characteristics of each item, such as its features, price, popularity, and ratings. Using these data, the system can generate recommendations tailored to the user's interests without collecting any personal information from the user. However, knowledge-based systems require access to the user's explicit knowledge about items and preferences to make recommendations. This means that the system has access to the user's sensitive information, which can be a serious concern from the user's perspective [53; 54]. * **Collaborative filtering**: This type of FRS prioritizes user privacy while providing effective and accurate recommendations [55]. It uses the user's data to generate personalized recommendations [56; 57]. Moreover, the system uses data from similar users to make recommendations to the user. This means that the system can make accurate recommendations by accessing limited users' data or having to share it with other users or systems. The collaborative filtering approach works by first identifying similar users who have the same demographic characteristics, tastes, or preferences [16; 58]. Next, the system takes the data from these similar users and uses them to make recommendations to the user. This data might include past purchases, ratings, or reviews. The system then uses these data to develop recommendations for the user [59].