Those homomorphic encryption schemes supporting single instruction multiple data(SIMD)operations effec-tively enhance the amortized efficiency of ciphertext computations,yet the structure of ciphertexts leads to high complex-ity in matrix operations.In many applications,employing plaintext-ciphertext matrix operations can achieve priva-cy-preserving computing.Based on this,a plaintext-ciphertext matrix multiplication scheme for matrices of arbitrary dimen-sion was proposed.The resulting ciphertext was computed through steps such as encoding the plaintext matrix,transforming the dimensions of the encrypted matrix,etc.Compared to the best-known encrypted matrix multiplication algorithm for square matrices proposed by Jiang et al.,the proposed scheme supported matrix multiplication of arbitrary dimension,and consecutive matrix multiplications.Both theoretical analysis and experimental results show that the proposed scheme re-quires less rotations on ciphertexts and hence features higher efficiency.When applied to a privacy-preserving Bayesian clas-sifier,the proposed scheme can complete classification tasks with higher security parameters and reduced running time.
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