Privacy-aware supervised classification: An informative subspace based multi-objective approach

Sharing the raw or an abstract representation of a labelled dataset on cloud platforms can potentially expose sensitive information of the data to an adversary, e.g., in the case of an emotion classification task from text, an adversary-agnostic abstract representation of the text data may eventually lead an adversary to identify the demographics of the authors, such as their gender and age. In this paper, we propose a universal defense mechanism against such malicious attempts of stealing sensitive information from data shared on cloud platforms. More specifically, our proposed method employs an informative subspace based multi-objective approach to obtain a sensitive information aware encoding of the data representation. A number of experiments conducted on both standard text and image datasets demonstrate that our proposed approach is able to reduce the effectiveness of the adversarial task (i.e., in other words is able to better protect the sensitive information of the data) without significantly reducing the effectiveness of the primary task itself.