{'fraction_non_alphanumeric': 0.011326860841423949, 'fraction_numerical': 0.0, 'mean_word_length': 5.447916666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Applications of assoiation rules hiding heuristic approaches

Data Mining allows large database owners to extract useful knowledge that could not be deduced with traditional approaches like statistics. However, these sometimes reveal sensitive knowledge or preach individual privacies. The term sanitization is given to the process of changing original database into another one from which we can mine without exposing sensitive knowledge. In this paper, we give a detailed explanation of some heuristic approaches for this purpose. We applied them on a number of publically available datasets and examine the results.