{'fraction_non_alphanumeric': 0.011482254697286013, 'fraction_numerical': 0.0, 'mean_word_length': 5.568493150684931, '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}}
A Survey on Privacy Preserving Decision Tree Classifier

In recent year’s privacy preservation in data mining has become an important issue. A new class of data mining method called privacy preserving data mining algorithm has been developed. The aim of these algorithms is protecting the sensitive information in data while extracting knowledge from large amount of data. The extracted knowledge is generally expressed in the form of cluster, decision tree or association rule allow one to mine the information. Several data modification technique like randomization method, anonymization method, distributed privacy technique have been developed to incorporating privacy mechanism and allow to hide sensitive pattern or itemset before data mining process is executed. This paper mainly focuses on general classification technique decision tree classifier for preserving privacy. It presents a survey on decision tree learning on various privacy techniques.