Self-adaptive Privacy Concern Detection for User-generated Content

TL;DR

This paper introduces a self-adaptive method for detecting user privacy concerns based on personality traits, enabling personalized differential privacy protection especially for cold-start users.

Contribution

It proposes a novel self-adaptive privacy concern detection approach that personalizes noise addition in differential privacy based on individual personality, improving privacy protection accuracy.

Findings

Effective privacy concern detection for cold-start users

Personalized noise addition enhances privacy without sacrificing data utility

Outperforms uniform privacy protection methods

Abstract

To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual's sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).