Yes, the techniques developed in this paper can be combined with other privacy-enhancing methods, such as dimensionality reduction, to further improve the accuracy-privacy tradeoff. Dimensionality reduction techniques, such as Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE), can help mitigate the curse of dimensionality, which often exacerbates the challenges of achieving differential privacy. By reducing the dimensionality of the data, these techniques can enhance the signal-to-noise ratio, making it easier for differentially private algorithms to learn meaningful patterns while maintaining privacy.
Integrating dimensionality reduction with the semi-DP algorithms proposed in this work could lead to more efficient training processes, as the reduced feature space would require less noise to achieve the same level of privacy. This could result in improved model accuracy, as the algorithms would be able to focus on the most informative features of the data. Additionally, dimensionality reduction can help in visualizing the data, allowing for better understanding and interpretation of the model's performance in a privacy-preserving context.
Furthermore, combining these techniques could also facilitate the development of hybrid models that leverage both public and private data more effectively. For instance, one could first apply dimensionality reduction to the public data before integrating it with the private data, ensuring that the most relevant information is preserved while minimizing the risk of privacy breaches. Overall, the synergy between the techniques developed in this paper and dimensionality reduction methods presents a promising avenue for enhancing the accuracy-privacy tradeoff in differentially private machine learning.