Abstract
The increasing complexity and ubiquity in user connectivity, computing environments, information content, and software, mobile, and web applications transfers the responsibility of privacy management to the individuals. Hence, making it extremely difficult for users to maintain the intelligent and targeted level of privacy protection that they need and desire, while simultaneously maintaining their ability to optimally function. Thus, there is a critical need to develop intelligent, automated, and adaptable privacy management systems that can assist users in managing and protecting their sensitive data in the increasingly complex situations and environments that they find themselves in. This work is a first step in exploring the development of such a system, specifically how user personality traits and other characteristics can be used to help automate determination of user sharing preferences for a variety of user data and situations. The Big-Five personality traits of openness, conscientiousness, extroversion, agreeableness, and neuroticism are examined and used as inputs into several popular machine learning algorithms in order to assess their ability to elicit and predict user privacy preferences. Our results show that the Big-Five personality traits can be used to significantly improve the prediction of user privacy preferences in a number of contexts and situations, and so using machine learning approaches to automate the setting of user privacy preferences has the potential to greatly reduce the burden on users while simultaneously improving the accuracy of their privacy preferences and security.
Original language | English |
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Title of host publication | Proceedings - 2017 IEEE Symposium on Privacy-Aware Computing, PAC 2017 |
Publisher | IEEE |
Pages | 84-95 |
Number of pages | 12 |
ISBN (Electronic) | 9781538610275 |
DOIs | |
Publication status | Published - 4 Dec 2017 |
Externally published | Yes |
Event | 1st IEEE Symposium on Privacy-Aware Computing, PAC 2017 - Washington, United States Duration: 1 Aug 2017 → 3 Aug 2017 |
Conference
Conference | 1st IEEE Symposium on Privacy-Aware Computing, PAC 2017 |
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Country/Territory | United States |
City | Washington |
Period | 1/08/17 → 3/08/17 |
Keywords
- machine learning
- personality trait
- privacy preference