# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 4 Applications and Target Industry  
The FRS is a powerful tool that can be utilized in various applications and target industries. It enables secure and privacy-preserving data sharing for big data and provides real-time recommendations across multiple sources [136]. However, sharing data means that users must reveal their private information, such as name and address, which can easily be taken advantage of by third-party companies [137; 138]. In particular, this private  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Privacy Type** & **Privacy Target** & **Privacy** & **C/C Costs** \\ \hline
[34; 120; 121; 122; 123] & Differential Privacy & Ratings & High & Low \\ \hline
[25; 86; 87; 88; 111; 124] & SMPC & Ratings & Low & High \\ \hline
[31; 35; 73; 125; 126; 127] & Homomorphic Encryption & High-Order Graphs Social Features Ratings & Moderate & High \\ \hline
[103; 128; 129; 130] & Tokenization & Prediction & Low & Low \\ \hline
[24; 131] & Anonymization & User’s data & High & High \\ \hline
[72; 132; 133; 134; 135] & Pseudonymization & User’s data & Moderate & Low \\ \hline \hline \end{tabular} In column \"Privacy\", we compare privacy with respect to accuracy. High means a good tradeoff between privacy and accuracy. Moderate means high privacy, but lower accuracy. Low means poor privacy and poor accuracy. C/C stands for communication/computation costs.  
\end{table}
Table 2: Comparison between several privacy-preserving techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Privacy-Preserving Technique** & **Description** & **Domain** & **Platform** & **Environment** \\ \hline \multirow{4}{*}{Differential Privacy} & A technique that adds noise to the data to protect individual privacy while still allowing for useful analysis & Healthcare, social science, finance & Google, Apple, Microsoft & Cloud computing, big data \\ \hline \multirow{4}{*}{Secure Multi-Party Computation} & A technique that allows multiple parties to jointly compute a function without revealing their inputs to each other & Banking, insurance, healthcare & Intel SGX, IBM Homomorphic Encryption Toolkit & Cloud computing, IoT \\ \hline \multirow{4}{*}{Homomorphic Encryption} & A technique that allows computations to be performed on encrypted data without decrypting them & Healthcare, finance, government & Microsoft SEAL, IBM Homomorphic Encryption Toolkit & Cloud Computing, IoT \\ \hline \multirow{4}{*}{Tokenization} & A technique that replaces sensitive data with non-sensitive data, while preserving their meaning & Payment processing, E-commerce & Stripe, Braintree & Cloud computing, web \\ \hline \multirow{4}{*}{Anonymization} & A technique that removes personally identifiable information from data & Social media, market research & Facebook, Twitter, Google & Web, mobile \\ \hline \multirow{4}{*}{Pseudonymization} & A technique that replaces personally identifiable information with a pseudonym & Healthcare, research & AWS, Azure & Cloud computing, big data \\ \hline \hline \end{tabular}
\end{table}
Table 3: Privacy-preserving techniques in various domains, platforms, and environments.  
information can be used in the retail industry to provide personalized product recommendations to customers, in the healthcare industry to identify potential treatments for patients [139], in the finance industry to suggest financial products to customers [140], and in content recommendation for E-commerce websites and for recommending movies and shows on streaming services [141]. In Figure 6, we present the potential applications and target industries of FRSs and outline their details in the following subsections. In Table 4, we present potential real-time applications and targeted industries of FRSs.