A study on the privacy dilemma overcoming method of voluntary personal information leakage in relational SNS: data-based privacy leakage risk score development

Abstract

Abstract The advancement of internet technology has facilitated the emergence of relational Social Network Services (SNS), offering services based on individuals' social connections. SNS users utilize personal information as a means of self-expression, thereby constructing their own social networks. However, the proliferation of personal information breaches has emerged as a significant contemporary concern due to the escalating use of SNS platforms. Recent incidents predominantly involve the collection and dissemination of information voluntarily disclosed on SNS, rather than by hacking. Despite the imperative need to forestall such breaches, there is a dearth of empirically applicable methodologies to gauge the risk of personal information leakage. Prior research methodologies for quantitatively assessing breach risk have predominantly concentrated on evaluating personal profiles alone, with limited consideration given to the potential identifiability of personal information embedded within uploaded content. Furthermore, these studies have often relied on surveys to ascertain users' perceptions of personal information leakage risk, hereby constraining their practical applicability and difficult to fulfill the objective of preventing personal information breaches. Hence, this study proposes a method for estimating privacy leakage risk based on the privacy-dilemma framework, which underscores the dilemmas SNS users encounter in managing both personal profiles and content data. Leveraging Social Network Analysis (SNA) to capture the nuances of relational SNS characteristics, we aim to enhance methodologies proposed in previous studies. The Multiple Regression Quadratic Assignment Procedure (MR-QAP) analysis is employed to delineate the factors influencing the risk score. This methodological approach holds promise in furnishing practical insights into privacy protection.