Reversible hidden data access algorithm in cloud computing environment

At present, the data filtering quality of reversible hidden data access algorithm based on column store database is not guaranteed, and the location accuracy and data access security of reversible hidden data are low. In this paper, the whitening vector is obtained by processing the sample length of the observed data signal. By using the nonlinear robust function, the data projection is realized, the judged threshold of projection data is constructed, an matrix with adaptive filter characteristic is set up, and the high quality of filtering results are output; the parameters between three anchor nodes and the location of reversible hidden data are measured, and the artificial bee colony optimization neural network is used for modeling and forecasting the ranging error, and determine the weights according to the results, so that on the basis of the three edge location algorithm, the positioning accuracy of the data is to further improve; through the establishment of authorized institutions, producing key, off-line encryption, online encryption, ciphertext conversion, decrypt ion and other aspects, the security of access data is completed. The experiment shows that the algorithm can effectively improve the quality of data filtering and positioning accuracy and the security of data access is also better than that of the current algorithm.