# Utilizing Noise Addition for Data Privacy, an Overview

arXiv: https://arxiv.org/abs/1309.3958 · HTML: https://mlsanity.com/paper/1309.3958/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2013.

## Built on by (most cited first, 6 of 6)

- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [HiddenSpeaker: Generate Imperceptible Unlearnable Audios for Speaker Verification System](https://mlsanity.com/paper/2405.15655/) (2024)
- [Privacy Enhancement for Cloud-Based Few-Shot Learning](https://mlsanity.com/paper/2205.07864/) (2022)
- [Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation method](https://mlsanity.com/paper/2310.06571/) (2023)
- [Graph Structure Learning with Privacy Guarantees for Open Graph Data](https://mlsanity.com/paper/2507.19116/) (2025)
- [Enhancing Noise Robustness of Parkinson's Disease Telemonitoring via Contrastive Feature Augmentation](https://mlsanity.com/paper/2510.01588/) (2025)

## Abstract (arXiv)

The internet is increasingly becoming a standard for both the production and consumption of data while at the same time cyber-crime involving the theft of private data is growing. Therefore in efforts to securely transact in data, privacy and security concerns must be taken into account to ensure that the confidentiality of individuals and entities involved is not compromised, and that the data published is compliant to privacy laws. In this paper, we take a look at noise addition as one of the data privacy providing techniques. Our endeavor in this overview is to give a foundational perspective on noise addition data privacy techniques, provide statistical consideration for noise addition techniques and look at the current state of the art in the field, while outlining future areas of research.