Voice assistants overhear conversations, and a consent management mechanism is required. Consent management can be implemented using speaker recognition. Users that do not give consent enroll their voice, and all their further recordings are discarded. Building speaker recognition-based consent management is challenging as dynamic registration, removal, and reregistration of speakers must be efficiently handled. This work proposes a consent management system addressing the aforementioned challenges. A contrastive-based training is applied to learn the underlying speaker equivariance inductive bias. The contrastive features for buckets of speakers are trained a few steps into each iteration and act as replay buffers. These features are progressively selected using a multi-strided random sampler for classification. Moreover, new methods for dynamic registration using a portion of old utterances, removal, and reregistration of speakers are proposed. The results verify memory efficiency and dynamic capabilities of the proposed methods and outperform the existing approaches from the literature in terms of convergence rate and number of required parameters.