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User attributes such as gender, age, and race, may allow an adversary to form users\u2019 communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users\u2019 community can occur from the privacy preserved published data. Each item of user attributes impacts users\u2019 community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users\u2019 unique identifications and sensitive information inferences more easily, and their presence in published data increases users\u2019 community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user\u2019s community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users\u2019 community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user\u2019s dataset to effectively preserve users\u2019 community privacy. Data generalization is performed adaptively by considering both user attributes\u2019 susceptibility and entropy simultaneously. The proposed algorithm controls over-generalization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users\u2019 community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.<\/jats:p>","DOI":"10.1007\/s10489-020-01656-w","type":"journal-article","created":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T21:07:01Z","timestamp":1584047221000},"page":"2555-2574","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data"],"prefix":"10.1007","volume":"50","author":[{"given":"Abdul","family":"Majeed","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4698-6018","authenticated-orcid":false,"given":"Sungchang","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,12]]},"reference":[{"key":"1656_CR1","doi-asserted-by":"publisher","unstructured":"Wieringa J, Kannan PK, Ma X, Reutterer T, Risselada H, Skiera B (2019) Data analytics in a privacy-concerned world. 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