An automated model to score the privacy of unstructured information—Social media case. Issue 92 (May 2020)

• Record Type: Journal Article
• Authors: Aghasian, Erfan; Garg, Saurabh; Montgomery, James
• Is Part Of: Computers & security. Issue 92(2020)
• Publication Date: 2020-05
• Subjects: Privacy -- Social networks -- Unstructured data -- Data privacy score -- Sentiment analysis -- Machine learning; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2020.101778
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: One of the common forms of data which is shared by online social media users is free-text formats including comments, posts, blogs and tweets. While users mostly share this unstructured data with their preferred social groups, this textual data may contain sensitive information such as their political or religious views, job details, their opinions and emotions and so on. Hence, sharing this unstructured data can escalate privacy risks and concerns for social media users. Analyses the privacy of unstructured data occurred from textual information comes with difficulties as understanding the calculation metrics are challenging. Although there are various studies on privacy evaluation from the extracted structured information from unstructured data, there are limited privacy scoring methods concentrating on the views of the individuals and cannot satisfy the privacy scoring of shared unstructured data in social networks appropriately. Here, in this paper, we propose an automated fuzzy-based model that can extract the privacy-related features as well as the related shared structured data and measure and warn users regarding the textual data privacy risks they have shared in online social platforms. The proposed model can facilitate mitigation actions for users' free-format texts shared in various social networks. The evaluation of the study indicates that the proposed model can measure the users' privacy risk in a more accurate manner compared with previously proposed methods and available commercialised software in the domain.