# PPM: A Privacy Prediction Model for Online Social Networks

arXiv: https://arxiv.org/abs/1606.07463 · HTML: https://mlsanity.com/paper/1606.07463/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [AI-driven Personalized Privacy Assistants: a Systematic Literature Review](https://mlsanity.com/paper/2502.07693/) (2025)

## Abstract (arXiv)

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.