Federated Learning (FL) has gained considerable interest due to its capacity to allow multiple participants to train a model without centralizing their data. However, due to the number of entities involved and the data sensitivity (e.g., in healthcare or military fields), there is a need to ensure some security and privacy requirements using specific tools. The problem is that combining these different mechanisms is not trivial. In particular, protecting intellectual property, using watermarking, with encrypted parameters is a problem that has been recently investigated. Even if the technical issue behind the problem of embedding a watermark in the ciphertext is resolved, some facets of the problem have not been covered. Starting from the existing solution [23], we propose a FL watermarking framework compatible with homomorphic encryption as a privacy mechanism that integrates two important functionalities in such a way as to complete the previous framework. On one hand, using the blinding computation principle, we allow the server to compute the watermark success rate of the extracted encrypted watermark at each round allowing it to follow the correct course of the embedding. On the other hand, we consolidate the proof of ownership by incorporating saves of the watermarking and model information in the blockchain. To diversify our proof of concept, we conduct experiments on three classification models to demonstrate the generality of our framework.