Technical perspective on 'Differentially Private Substring andDocument Counting'

Differential privacy has become the de facto privacy standard, as it is widely employed by various organizations. However, only a few research works have focused on strings (e.g., [1, 2, 5]). This is surprising, given that strings are fundamental in modeling, for example, genomic sequences, mobility traces, or text logs. One of the key tasks in string database analysis is to count the occurrences of string fragments in the database: for instance, extracting frequent patterns or ??-grams, or publishing word frequency statistics. Most existing differentially private algorithms for string mining are largely heuristic and come with little or no worst-case error analysis, which is a major limitation given the fact that strings appear in key domains for decision making.