Boosting Privacy and Performance in Differentially Private Heavy Hitter Detection

Improving AI Algorithm Performance for Heavy Hitter Detection with Privacy

In the field of artificial intelligence (AI), there are practical heuristics that focus on enhancing the effectiveness of prefix-tree based algorithms used for differentially private heavy hitter detection. These algorithms aim to identify the most frequently occurring data points across multiple users’ data, while preserving privacy through aggregate and local differential privacy measures.

The Significance of Improving Algorithm Performance

Enhancing algorithm performance is crucial for achieving accurate heavy hitter detection. By optimizing the algorithm, we can gather valuable insights from extensive data sets, such as the popular platform Reddit, specifically in the task of learning the most frequent words.

Introducing an Adaptive Hyperparameter Tuning Algorithm

Our research proposes an innovative solution in the form of an adaptive hyperparameter tuning algorithm. This algorithm focuses on improving the performance of the existing prefix-tree based algorithm while adhering to computational, communication, and aggregate privacy constraints.

Exploring Data-Selection Schemes and Deny Lists

To further enhance the algorithm’s efficiency, different data-selection schemes are explored along with the introduction of deny lists during multiple runs of the algorithm. These enhancements aim to refine the heavy hitter detection process and provide more accurate results.

Experimental Testing on the Reddit Dataset

To validate the effectiveness of our proposed improvements, extensive experimentation is conducted using the vast Reddit dataset. The primary objective is to identify and learn the most frequent words, showcasing the algorithm’s improved performance.

In summary, our research focuses on practical considerations to optimize prefix-tree based algorithms for differentially private heavy hitter detection. By introducing an adaptive hyperparameter tuning algorithm and exploring various data-selection schemes, we aim to enhance the efficiency and accuracy of the algorithm. Through extensive experimentation on the Reddit dataset, we demonstrate the tangible benefits of these improvements in the task of learning most frequent words.

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