# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 2 Overview of the FRS
### Categories of FRS
One major privacy issue with collaborative filtering is that it requires access to sensitive user behavior data to make recommendations. These data can include information about the user's past purchases, searches, and interactions with other users. Therefore, without proper privacy measures in place, these data can be vulnerable to unauthorized access, misuse, or disclosure [60]. * **Context-aware recommendation**: This type of FRS uses context-aware techniques to help tailor the recommendations to the users in a way that is more meaningful and tailored to their interests and needs [61; 62]. Context-aware recommendation systems are designed to consider the user's current context when making recommendations, such as his/her location, time, and surrounding environment [63; 64]. This allows the system to make more accurate and personalized recommendations that better suit the users' needs and preferences [65]. By taking into account the user's current context, the system can provide recommendations that are more accurate and relevant to the user's needs [66; 67]. This type of system can help reduce the amount of data that need to be collected and stored by the system, as the data used to generate the recommendations are already present in the user's context [68]. The privacy concern with context-aware recommendations is that they require access to a wide range of user data, including location, device information, and social media activity, to make personalized recommendations. These data can be sensitive and reveal private information about the user's behavior, interests, and preferences. Therefore, it is important for context-aware recommendation systems to implement robust privacy-preserving techniques to protect user privacy while still providing effective recommendations. * **Hybrid recommendation systems**: Hybrid recommendation systems are a type of FRS that combine the capabilities of traditional centralized and distributed recommendation systems to provide users with the best of both worlds [3]. Hybrid recommendation systems enable users to make use of personalized recommendations while also protecting their privacy. In a hybrid system, the data are stored in a central repository, and the recommendation algorithm is executed by a federated learning system [69]. The central repository stores all of the user's profile data, and the federated learning system executes the recommendation algorithm without any user data being shared between the two. This allows the system to remain secure and the user data to remain private. Besides, the hybrid system allows different algorithms to be used. For example, collaborative filtering can be used in conjunction with content-based filtering to provide a more accurate recommendation [70]. Furthermore, the system can be adapted to different data sources, such as social media and web analytics. This type of system also has the advantage of scalability. As new users join the system, the recommendation algorithm can be updated without requiring additional user data to be shared [71]. This makes the system more efficient and effective and allows for more users to be accommodated. The primary privacy concern with hybrid recommendation systems is the extensive collection of user data from various sources required to make accurate and diverse recommendations. These data can include sensitive user behavior, profile, and contextual data, which if not properly protected, can lead to unauthorized access and unintended disclosure of personal information. Thus, such systems must prioritize the implementation of robust privacy-preserving techniques, such as data anonymization, homomorphic encryption, and differential privacy, to mitigate the risks of data breaches and ensure that user privacy is not compromised.