{'fraction_non_alphanumeric': 0.02121433796634967, 'fraction_numerical': 0.0, 'mean_word_length': 5.483412322274882, '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}}
Differential-Privacy-Based Citizen Privacy Preservation in E-Government Applications

In the era of big data, opening of the information in e-government applications and sharing of the information resources among government departments have become the requirements of the times, while how to protect the privacy of citizens has become one of the focus issues of the government and public. To prevent the disclosure or abuse of the citizens' privacy information, the citizens' privacy needs to be preserved in the process of information opening and sharing. However, most of the existing privacy preserving models cannot to be used to resist attacks with continuously growing background knowledge. This paper presents the method of applying differential privacy to protect the citizens' privacy information. By generalizing the citizens' sensitive information, the anonymity sets satisfying (K, L)-anonymity model are constructed, then differential method is used to add Laplace noise in the anonymity sets. Thus the citizen's privacy information can be protected even if the attacker gets strong background knowledge. Because the grouped information reduces the sensitivity of the query, the availability of citizens' information after adding noise can be guaranteed. The steps and usefulness of the discussed privacy preservation method is illustrated by an example.