Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

Table 1

Summary of literature survey.

Algorithm	Advantage	Limitation
Pufferfish [27]	The algorithm takes into account the correlation between data	Does not satisfy differential privacy
PCA [24–26], DFT [15], and DWT [22,23]	Under the premise of keeping the main characteristics of the sequence unchanged, the correlation time series is transformed into another independent domain for processing	Independent noise is added and the sequence correlation is destroyed to some extent
CIM [12]	Literature [12] proposed correlated sensitivity to reduce noise and utilized a correlation coefficient matrix to describe the correlation of a series	It is only applicable to the publication of histogram statistics
CTS-DP [16]	The correlation noise is added to the original time-series data	Dynamic data cannot be processed and privacy protection is inadequate