{'fraction_non_alphanumeric': 0.013562386980108499, 'fraction_numerical': 0.0, 'mean_word_length': 5.254237288135593, '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 Novel Approach to Evade Attacks in Online Social Networks

Anonymization of published social network data has become an important research topic nowadays. The major concern is to publish data of individuals in such a manner that the released table not only provides the essential information to be used by researchers but also prevents the disclosure of sensitive information. Various techniques like K-anonymity, L-diversity and T-closeness have been proposed to achieve anonymization of the data. K-anonymity suffers from various limitations such as it cannot prevent attribute disclosure and also results in homogeneity and background knowledge attacks. This paper mainly deals with handling these two attacks that break the privacy constraints of K-anonymity. The proposed technique has been evaluated using real-time database gathered from Twitter social network. Experimental results show that the proposed technique not only prevents identity and attribute disclosure but at the same time is capable of handling attacks on K-anonymity by resulting in minimum information loss to the original data.