SPFM: Scalable and Privacy-Preserving Friend Matching in Mobile Cloud,Profile (e.g., contact list, interest, and mobility) matching is more than important for fostering the wide use of mobile social networks. The social networks such as Facebook, Line, or WeChat recommend the friends for the users based on users personal data such as common contact list or mobility traces. However, outsourcing users’ personal information to the cloud for friend matching will raise a serious privacy concern due to the potential risk of data abusing. In this SPFM: Scalable and Privacy-preserving Friend Matching paper, we propose a novel scalable and privacy-preserving friend matching (SPFM) protocol, which aims to provide a scalable friend matching and recommendation solutions without revealing the users personal data to the cloud. The private profile matching problem could then be converted into Private Set Intersection (PSI), or Private Set Intersection Cardinality (PSICA). However, we argue that the existing works may fail to work in practice due to the following two reasons. Firstly, the best practice in industry for friends recommendation is a multiple-users matching problem rather than a two-party matching problem. Some pre-share parameters between users are more likely to leak. Secondly, most of the existing works involve multiple rounds of protocols, which will suffer from a serious performance challenge.