Time Series Synthesis Using the Matrix Profile for Anonymization
Audrey Der Chin-Chia Michael Yeh Yan Zheng Junpeng Wang Huiyuan Chen Zhongfang Zhuang Liang Wang Wei Zhang Eamonn Keogh
Publishing and sharing data is crucial for the data mining community,
allowing collaboration and driving open innovation. However, many researchers
cannot release their data due to privacy regulations or fear of leaking
confidential business information. To alleviate such issues, we propose the
Time Series Synthesis Using the Matrix Profile (TSSUMP) method, where
synthesized time series can be released in lieu of the original data. The
TSSUMP method synthesizes time series by preserving similarity join information
(i.e., Matrix Profile) while reducing the correlation between the synthesized
and the original time series. As a result, neither the values for the
individual time steps nor the local patterns (or shapes) from the original data
can be recovered, yet the resulting data can be used for downstream tasks that
data analysts are interested in. We concentrate on similarity joins because
they are one of the most widely applied time series data mining routines across
different data mining tasks. We test our method on a case study of ECG and
gender masking prediction. In this case study, the gender information is not
only removed from the synthesized time series, but the synthesized time series
also preserves enough information from the original time series. As a result,
unmodified data mining tools can obtain near-identical performance on the
synthesized time series as on the original time series.