Practical considerations on using private sampling for synthetic data
- URL: http://arxiv.org/abs/2312.07139v1
- Date: Tue, 12 Dec 2023 10:20:04 GMT
- Title: Practical considerations on using private sampling for synthetic data
- Authors: Clément Pierquin, Bastien Zimmermann, Matthieu Boussard,
- Abstract summary: Differential privacy for synthetic data generation has received much attention due to the ability of preserving privacy while freely using the synthetic data.
Private sampling is the first noise-free method to construct differentially private synthetic data with rigorous bounds for privacy and accuracy.
We provide an implementation of the private sampling algorithm and discuss the realism of its constraints in practical cases.
- Score: 1.3654846342364308
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial intelligence and data access are already mainstream. One of the main challenges when designing an artificial intelligence or disclosing content from a database is preserving the privacy of individuals who participate in the process. Differential privacy for synthetic data generation has received much attention due to the ability of preserving privacy while freely using the synthetic data. Private sampling is the first noise-free method to construct differentially private synthetic data with rigorous bounds for privacy and accuracy. However, this synthetic data generation method comes with constraints which seem unrealistic and not applicable for real-world datasets. In this paper, we provide an implementation of the private sampling algorithm and discuss the realism of its constraints in practical cases.
Related papers
- Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data [51.41288763521186]
Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources.
RAG systems may face severe privacy risks when retrieving private data.
We propose using synthetic data as a privacy-preserving alternative for the retrieval data.
arXiv Detail & Related papers (2024-06-20T22:53:09Z) - FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning [54.26614091429253]
Federated instruction tuning (FedIT) is a promising solution, by consolidating collaborative training across multiple data owners.
FedIT encounters limitations such as scarcity of instructional data and risk of exposure to training data extraction attacks.
We propose FewFedPIT, designed to simultaneously enhance privacy protection and model performance of federated few-shot learning.
arXiv Detail & Related papers (2024-03-10T08:41:22Z) - Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data
Generation and Evaluation in Learning Analytics [0.412484724941528]
Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonymization and data misuse.
Synthetic data emerges as a potential remedy, offering robust privacy protection.
Prior LA research on synthetic data lacks thorough evaluation, essential for assessing the delicate balance between privacy and data utility.
arXiv Detail & Related papers (2024-01-12T20:27:55Z) - A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing.
Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data.
Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
arXiv Detail & Related papers (2023-09-27T14:38:16Z) - The Use of Synthetic Data to Train AI Models: Opportunities and Risks
for Sustainable Development [0.6906005491572401]
This paper investigates the policies governing the creation, utilization, and dissemination of synthetic data.
A well crafted synthetic data policy must strike a balance between privacy concerns and the utility of data.
arXiv Detail & Related papers (2023-08-31T23:18:53Z) - Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic
Data [91.52783572568214]
Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs.
We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
arXiv Detail & Related papers (2023-04-07T16:38:40Z) - Synthetic Data: Methods, Use Cases, and Risks [11.413309528464632]
A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead.
We provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology.
arXiv Detail & Related papers (2023-03-01T16:35:33Z) - Private Set Generation with Discriminative Information [63.851085173614]
Differentially private data generation is a promising solution to the data privacy challenge.
Existing private generative models are struggling with the utility of synthetic samples.
We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
arXiv Detail & Related papers (2022-11-07T10:02:55Z) - Synthetic Text Generation with Differential Privacy: A Simple and
Practical Recipe [32.63295550058343]
We show that a simple and practical recipe in the text domain is effective in generating useful synthetic text with strong privacy protection.
Our method produces synthetic text that is competitive in terms of utility with its non-private counterpart.
arXiv Detail & Related papers (2022-10-25T21:21:17Z) - Representative & Fair Synthetic Data [68.8204255655161]
We present a framework to incorporate fairness constraints into the self-supervised learning process.
We generate a representative as well as fair version of the UCI Adult census data set.
We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
arXiv Detail & Related papers (2021-04-07T09:19:46Z) - Really Useful Synthetic Data -- A Framework to Evaluate the Quality of
Differentially Private Synthetic Data [2.538209532048867]
Recent advances in generating synthetic data that allow to add principled ways of protecting privacy are a crucial step in sharing statistical information in a privacy preserving way.
To further optimise the inherent trade-off between data privacy and data quality, it is necessary to think closely about the latter.
We develop a framework to evaluate the quality of differentially private synthetic data from an applied researcher's perspective.
arXiv Detail & Related papers (2020-04-16T16:24:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.