#maskUp: Selective Attribute Encryption for Sensitive Vocalization for English language on Social Media Platforms

TL;DR

#maskUp is a novel method that uses selective attribute encryption and natural language processing to protect victims' privacy in social media vocalizations, enabling secure reporting to authorities while discouraging bullying.

Contribution

It introduces a first-of-its-kind privacy-preserving technique for masking sensitive vocalization attributes in social media communications.

Findings

Successfully masks sensitive information in sample datasets

Demonstrates practical implementation on continual learning tasks

Validates privacy protection and secure reporting capabilities

Abstract

Social media has become a platform for people to stand up and raise their voices against social and criminal acts. Vocalization of such information has allowed the investigation and identification of criminals. However, revealing such sensitive information may jeopardize the victim's safety. We propose #maskUp, a safe method for information communication in a secure fashion to the relevant authorities, discouraging potential bullying of the victim. This would ensure security by conserving their privacy through natural language processing supplemented with selective encryption for sensitive attribute masking. To our knowledge, this is the first work that aims to protect the privacy of the victims by masking their private details as well as emboldening them to come forward to report crimes. The use of masking technology allows only binding authorities to view/un-mask this data. We construct and evaluate the proposed methodology on continual learning tasks, allowing practical implementation of the same in a real-world scenario. #maskUp successfully demonstrates this integration on sample datasets validating the presented objective.