Online Social Networks (OSNs) have become an important part of daily digital interactions for more than half billion users around the world. Online social networks exhibit many of the characteristics of human societies in terms of forming relationships and sharing personal information. However, current OSNs mainly assume binary, static, and symmetric relationship of equal value between the connected users. In human societies, social relationships are of varying tie strength, dynamic, and asymmetric in nature. The lack of an effective mechanism to represent diversity in social relationships leads to undesirable consequences of users personal information leakage to the unwanted audience and raises privacy concerns. The issue of privacy has received significant attention in both the research literature and the mainstream media. In this dissertation, we conduct a user study to analyze users' attitude towards personal information disclosure in online social networks. The findings reveal that personal information disclosure depends on the quality of relationship among the users and it can be easily inferred from user interaction pattern in online social networks. We propose a theoretical framework that addresses the aforementioned issue from a social science perspective and exploits existing social theories of Goffman, Granovetter, and Nissenbaum to model social privacy for OSNs users. Based on this theoretical framework, we developed SOCPRI (SOCial PRIvacy) ontology to represent diversity in social relationships in online social networks. This model regulates personal information disclosure on the basis of the social role and the relationship quality between the OSNs users. The model is evaluated by translating competency questions into description logic (DL) queries to demonstrate the applicability of our approach. The results of ontology evaluation demonstrate the appropriateness of our ontology against proposed requirements.
Contextual Integrity and Tie Strength in Online Social Networks: Social Theory, User Study, Ontology, and Validation
2017
Abstract
Online Social Networks (OSNs) have become an important part of daily digital interactions for more than half billion users around the world. Online social networks exhibit many of the characteristics of human societies in terms of forming relationships and sharing personal information. However, current OSNs mainly assume binary, static, and symmetric relationship of equal value between the connected users. In human societies, social relationships are of varying tie strength, dynamic, and asymmetric in nature. The lack of an effective mechanism to represent diversity in social relationships leads to undesirable consequences of users personal information leakage to the unwanted audience and raises privacy concerns. The issue of privacy has received significant attention in both the research literature and the mainstream media. In this dissertation, we conduct a user study to analyze users' attitude towards personal information disclosure in online social networks. The findings reveal that personal information disclosure depends on the quality of relationship among the users and it can be easily inferred from user interaction pattern in online social networks. We propose a theoretical framework that addresses the aforementioned issue from a social science perspective and exploits existing social theories of Goffman, Granovetter, and Nissenbaum to model social privacy for OSNs users. Based on this theoretical framework, we developed SOCPRI (SOCial PRIvacy) ontology to represent diversity in social relationships in online social networks. This model regulates personal information disclosure on the basis of the social role and the relationship quality between the OSNs users. The model is evaluated by translating competency questions into description logic (DL) queries to demonstrate the applicability of our approach. The results of ontology evaluation demonstrate the appropriateness of our ontology against proposed requirements.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/313583
URN:NBN:IT:BNCF-313583