Shuffled Differential Privacy （SDP） models can balance the degree of privacy protection at user side and the usability of published results at server side. Therefore， they are more suitable for privacy-preserving big data collection and statistical publishing scenarios. Aiming at the problems of low shuffling efficiency and insufficient shuffling process security of the existing SDP frequency estimation methods， the following work was performed： firstly， an SDP Blind Signature Algorithm （SDPBSA） was designed on the basis of optimized elliptic curve to achieve discrimination of tampered or forged information， thereby improving the security of shuffling process. Then， a Matrix Column Rearrangement Transposition （MCRT） shuffling method was proposed to realize data shuffling by random matrix column rearrangement and matrix transposition operations， thereby improving the efficiency of shuffling process. Finally， above methods were combined to construct a complete SDP frequency estimation privacy protection framework — SM-SDP （SDP based on blind Signature and Matrix column rearrangement transposition）， and its privacy and error level were analyzed theoretically. Experimental results on datasets such as Normal， Zipf， and IPUMS （Integrated Public Use Microdata Series） demonstrate that the MCRT shuffling method improves the shuffling efficiency by about 1 to 2 orders of magnitude compared to shuffling methods such as Fisher-Yates， ORShuffle （Oblivious Recursive Shuffling）， and MRS （Message Random Shuffling）； SM-SDP framework reduces the Mean Squared Error （MSE） by 2 to 11 orders of magnitude in the presence of different proportions of malicious data compared to frequency estimation methods such as mixDUMP， PSDP （Personalized Differential Privacy in Shuffle model）， and HP-SDP （Histogram Publication with SDP）.