In this information era, data is considered as the new oil of the 21st century. Data is misused by organizations or countries for economic or political gain. The potential damage due to personal data privacy breach can be controlled at a political, social, and technological level. This research aims to provide a technical solution to evaluate and warn the user of a potential data breach via a software plugin which uses semantic web technologies and knowledge graphs.
This project aims to build a software that analyses the privacy risk of visiting and entering data into the website by considering the user's relationship with the organization and user-entered data into the web- site. This software generates a data sensitivity metric of the data entered and a privacy risk score associated with the website. Based on the sensitivity metric/privacy risk score, the user will be warned about the potential privacy risk via a software plugin in their web browser.
The user's relationship with the organization is analysed based on two sources, one is from the organisation's knowledge graph and an- other is from the user profile. The organisation's knowledge graph has knowledge about its website's category, web traffic rate, website's age, presence of adult content and its physical presence. The user profile has information such as nationality, date of birth, education details, professional experience details and user's browsing history. Among all these data, connection with respect to their physical location and their history impact the most, and additionally website's web traffic, website's age and website's category are major factors to be used to assess the privacy risk to the user.
The code in this repository has two folders
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Plugin: This folder has a Google chrome extension software code which is written in the JavaScript programming language. (client)
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Backend: This folder has ask-based backend software code which is written in the python programming language. (Server)
To test the approach and the developed framework, testing is done by 7 participants. Folder "Survey_feedback" has data files which consist of privacy risk score feedback by the participants.