Delphi Study of Risk to Individuals who Disclose Personal Information Online

A two-round Delphi study was conducted to explore priorities for addressing online risk to individuals. A corpus of literature was created based on 69 peer-reviewed articles about privacy risk and the privacy calculus published between 2014 and 2019. A cluster analysis of the resulting text-base using Pearson’s correlation coefficient resulted in seven broad topics. After two rounds of the Delphi survey with experts in information security and information literacy, the following topics were identified as priorities for further investigation: personalisation versus privacy; responsibility for privacy on social networks; measuring privacy risk; and perceptions of powerlessness and the resulting apathy. The Delphi approach provided clear conclusions about research topics and has potential as a tool for prioritising future research areas

Delphi Study of Risk to Individuals who Disclose Personal Information Online

A two-round Delphi study was conducted to explore priorities for addressing online risk to individuals. A corpus of literature was created based on 69 peer-reviewed articles about privacy risk and the privacy calculus published between 2014 and 2019. A cluster analysis of the resulting text-base using Pearson’s correlation coefficient resulted in seven broad topics. After two rounds of the Delphi survey with experts in information security and information literacy, the following topics were identified as priorities for further investigation: personalisation versus privacy; responsibility for privacy on social networks; measuring privacy risk; and perceptions of powerlessness and the resulting apathy. The Delphi approach provided clear conclusions about research topics and has potential as a tool for prioritising future research areas

Modeling privacy leakage risks in large-scale social networks

The current culture that encourages online dating, and interaction makes large-scale social network users vulnerable to miscellaneous personal identifiable information leakage. To this end, we take a first step toward modeling privacy leakages in large-scale social networks from both technical and economic perspectives. From a technical perspective, we use Markov chain to propose a dynamic attack-defense tree-based model, which is temporal-aware, to characterize an attack effort made by an attacker and a corresponding countermeasure responded by a social network security defender. From an economic perspective, we use static game theory to analyze the ultimate strategies taken by the attacker and the defender, where both rational participants tend to maximize their utilities, with respect to their attack/defense costs. To validate the proposed approach, we perform extensive experimental evaluations on three real-world data sets, triggered by the survey of over 300 volunteers involved, which illuminates the privacy risk management of contemporary social network service providers.Suguo Du, Xiaolong Li, Jinli Zhong, Lu Zhou, Minhui Xue, Haojin Zhu, and Limin Su