PPM: A Privacy Prediction Model for Online Social Networks

TL;DR

This paper introduces PPM, a privacy prediction model based on psychological principles, designed to help users make better privacy decisions on social networks by predicting their preferences and offering personalized advice.

Contribution

The paper presents a novel privacy prediction model rooted in psychological factors, enhancing privacy decision support in online social networks.

Findings

Identifies key psychological variables affecting disclosure behavior

Demonstrates the model's ability to predict user privacy preferences

Provides personalized privacy advice based on user-specific factors

Abstract

Online Social Networks (OSNs) have come to play an increasingly important role in our social lives, and their inherent privacy problems have become a major concern for users. Can we assist consumers in their privacy decision-making practices, for example by predicting their preferences and giving them personalized advice? To this end, we introduce PPM: a Privacy Prediction Model, rooted in psychological principles, which can be used to give users personalized advice regarding their privacy decision-making practices. Using this model, we study psychological variables that are known to affect users' disclosure behavior: the trustworthiness of the requester/information audience, the sharing tendency of the receiver/information holder, the sensitivity of the requested/shared information, the appropriateness of the request/sharing activities, as well as several more traditional contextual factors.