  Current speaker anonymization methods, especially with self-supervised
learning (SSL) models, require massive computational resources when hiding
speaker identity. This paper proposes an effective and parameter-efficient
speaker anonymization method based on recent End-to-End model reprogramming
technology. To improve the anonymization performance, we first extract speaker
representation from large SSL models as the speaker identifies. To hide the
speaker's identity, we reprogram the speaker representation by adapting the
speaker to a pseudo domain. Extensive experiments are carried out on the
VoicePrivacy Challenge (VPC) 2022 datasets to demonstrate the effectiveness of
our proposed parameter-efficient learning anonymization methods. Additionally,
while achieving comparable performance with the VPC 2022 strong baseline 1.b,
our approach consumes less computational resources during anonymization.
