  Sequential data is everywhere, and it can serve as a basis for research that
will lead to improved processes. For example, road infrastructure can be
improved by identifying bottlenecks in GPS data, or early diagnosis can be
improved by analyzing patterns of disease progression in medical data. The main
obstacle is that access and use of such data is usually limited or not
permitted at all due to concerns about violating user privacy, and rightly so.
Anonymizing sequence data is not a simple task, since a user creates an almost
unique signature over time. Existing anonymization methods reduce the quality
of information in order to maintain the level of anonymity required. Damage to
quality may disrupt patterns that appear in the original data and impair the
preservation of various characteristics. Since in many cases the researcher
does not need the data as is and instead is only interested in the patterns
that exist in the data, we propose PrivGen, an innovative method for generating
data that maintains patterns and characteristics of the source data. We
demonstrate that the data generation mechanism significantly limits the risk of
privacy infringement. Evaluating our method with real-world datasets shows that
its generated data preserves many characteristics of the data, including the
sequential model, as trained based on the source data. This suggests that the
data generated by our method could be used in place of actual data for various
types of analysis, maintaining user privacy and the data's integrity at the
same time.
