# Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets
## 6 Conclusions  
This paper presents a comprehensive evaluation of datasets spanning various domains. We employed our proposed LDP scheme based on BRR across different scenarios while estimating the joint probability distribution with a privacy budget set at 0.1. Our method consistently outperformed LOPUB and LOCOP. We suggest using the average absolute Pearson correlation coefficient (AAR) as a valuable tool for scrutinizing datasets before publication and assessing the performance of synthetic data algorithms. This approach helps prevent the release or sharing of datasets containing highly correlated attributes, safeguarding users' privacy. Particularly in datasets with a lower AAR, our method achieved a significant decrease in the average variant distance compared to LOPUB and LOCOP.  
We utilized the R-squared metric to evaluate the performance of LASSO regression and Bayesian Ridge Regression. The results demonstrate that BRR consistently outperforms both LOPUB and LOCOP, which are based on LASSO regression. This highlights BRR's greater stability across various joint probability distribution evaluations, privacy budgets, and AAR compared to LASSO, thanks to its effective handling of multicollinearity.  
In summary, our study reveals the nuanced performance of our proposed method, LOPUB, and LOCOP across diverse datasets, underscoring the significant influence of AAR on their efficacy. These findings stress the importance of aligning algorithmic choices with datasets' unique characteristics to achieve optimal results, especially in critical applications like healthcare, where precise predictions and considerations of attribute correlations are crucial. Importantly, our study highlights the need to avoid high correlation among attributes in datasets, as indicated by our results.  
In contemplating the future of this research, we anticipate a continuous process of refining and optimizing our proposed LDP scheme based on BRR. This entails addressing the intricate trade-off between accuracy and computational complexity across diverse scenarios, which includes variations in privacy budgets and attribute compositions. We envision further advancements aimed at enhancing the adaptability and efficacy of our approach in real-world applications. Additionally, we aim to extend the application of our LDP approach to generate accurate machine learning models.  
The importance and contribution of this work for the future are substantial. Our results demonstrate that BRR is able to present better performance than LOPUB and LOCOP when a strong privacy budget is used to protect the privacy of the users. These findings suggest that BRR can be an effective tool for privacy preservation in data publication, and it may have various applications where privacy is a concern.  
Conceptualization, A.H.-M.; Methodology, A.H.-M.; Software, A.H.-M.; Validation, A.H.-M.; Formal analysis, A.H.-M.; Investigation, A.H.-M.; Resources, H.K.; Data curation, A.H.-M.; Writing--original draft, A.H.-M.; Writing--review & editing, H.K.; Supervision, H.K.; Project administration, H.K.; Funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript. This work was supported by JST, CREST Grant Number JPMJCR21M1, Japan.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The source code for our Python experiments can be found at [[https://github.com/phdmatamoros/LDPusingBRR](https://github.com/phdmatamoros/LDPusingBRR)]([https://github.com/phdmatamoros/LDPusingBRR](https://github.com/phdmatamoros/LDPusingBRR)), accessible since 27 March 2024.  
The authors declare no conflict of interest.