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

arXiv: https://arxiv.org/abs/2211.08653 · HTML: https://mlsanity.com/paper/2211.08653/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [TENER: Adapting Transformer Encoder for Named Entity Recognition](https://mlsanity.com/paper/1911.04474/) (2019)
- [Assessing Demographic Bias in Named Entity Recognition](https://mlsanity.com/paper/2008.03415/) (2020)
- [Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition](https://mlsanity.com/paper/2107.11610/) (2021)
- [Boosting Named Entity Recognition with Neural Character Embeddings](https://mlsanity.com/paper/1505.05008/) (2015)
- [Recent Trends in Named Entity Recognition (NER)](https://mlsanity.com/paper/2101.11420/) (2021)
- [NERDA-Con: Extending NER models for Continual Learning -- Integrating Distinct Tasks and Updating Distribution Shifts](https://mlsanity.com/paper/2206.14607/) (2022)

## Abstract (arXiv)

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.