f train- ing samples. By using an MMV CS model, each sensor transmitted the updated aggregated parameters instead of the raw spectrum data to the central server to protect privacy. They demonstrated that the detection performance was as good as the scheme under the raw training samples, while significantly improving the communication and training efficiency. 11.2.5.2 Privacy Protection for SUs in SS Networks In [61], several location privacy-related attacks in CSS-based CRNs were first identified. Such attacks can threaten SUs' location privacy by correlating their sensing reports and their physical location. To prevent leakage of location privacy, a privacy-preserving frame- work was proposed. It was demonstrated that without efficient protection, the attackers could compromise a SU's location privacy at a success rate of more than 90%. A proposed privacy-preserving framework was further introduced and verified, which could signifi- cantly improve the location privacy of SUs with a minimal effect on the performance of collaborative sensing. To protect the location privacy of SUs while allowing them to sense vicinity spectrum availability, two location privacy-preserving schemes for database-driven CRNs were studied in [66]. The spectrum databases' structured nature and SUs' queries were exploited by those schemes to create a compact representation of databases that could be queried by SUs without requiring them to share their location with the database, thereby eliminating the possibility of location leakage. Based on whether a user is a member of a set or not, the first method, location privacy in database-driven CRNs (LPDB), constructed a compact version of the database and provided optimal location privacy to SUs in the coverage area. It achieved unconditional security with an acceptable communication overhead. The second method, LPDB with two servers (LPDBQS), minimized SUs' overhead with an additional network entity cost. The tradeoff between cost and performance provided more options for system design base