Utilizing Noise Addition for Data Privacy, an Overview
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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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Sep 2013.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 2.
6 papers.
- 2022Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluationcited by 40
- 2024HiddenSpeaker: Generate Imperceptible Unlearnable Audios for Speaker Verification Systemcited by 4
- 2022Privacy Enhancement for Cloud-Based Few-Shot Learningcited by 0
- 2023Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation methodcited by 0
- 2025Graph Structure Learning with Privacy Guarantees for Open Graph Datacited by 0
- 2025Enhancing Noise Robustness of Parkinson's Disease Telemonitoring via Contrastive Feature Augmentationcited by 0
- 2025Enhancing Noise Robustness of Parkinson's Disease Telemonitoring via Contrastive Feature Augmentation2 Oct 2025
- 2025Graph Structure Learning with Privacy Guarantees for Open Graph Data25 Jul 2025
- 2024HiddenSpeaker: Generate Imperceptible Unlearnable Audios for Speaker Verification System24 May 2024
- 2023Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation method10 Oct 2023
- 2022Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation24 Nov 2022
- 2022Privacy Enhancement for Cloud-Based Few-Shot Learning10 May 2022