  The demand for processing vast volumes of data has surged dramatically due to
the advancement of machine learning technology. Large-scale data processing
necessitates substantial computational resources, prompting individuals and
enterprises to turn to cloud services. Accompanying this trend is a growing
concern regarding data leakage and misuse. Homomorphic encryption (HE) is one
solution for safeguarding data privacy, enabling encrypted data to be processed
securely in the cloud. However, the encryption and decryption routines of some
HE schemes require considerable computational resources, presenting non-trivial
work for clients. In this paper, we propose an outsourced decryption protocol
for the prevailing RLWE-based fully homomorphic encryption schemes. The
protocol splits the original decryption into two routines, with the
computationally intensive part executed remotely by the cloud. Its security
relies on an invariant of the NTRU-search problem with a newly designed
blinding key distribution. Cryptographic analyses are conducted to configure
protocol parameters across varying security levels. Our experiments demonstrate
that the proposed protocol achieves up to a $67\%$ acceleration in the client's
local decryption, accompanied by a $50\%$ reduction in space usage.
