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

Algorithm 1

SW-ATS.

	Input: original time series X
	Output: time series to be published after adding noise
(1)	Read the original time series X and divide X into n subsequences using the sliding window length L, where .
(2)	for i = 1 to n:
(3)	Calculate the autocorrelation function of the subsequence .
(4)	According to the query function q, calculate the periodic sensitivity of the time-series data X, where is computed by equation (5).
(5)	Generate four IID Gauss white noise series , which have the same length as . In addition, , where .
(6)	Calculate , , , and , where .
(7)	.
(8)	Splice at the end of Z.
(9)	end for
(10)
(11)	Return