Bloom Filter Encoding for Machine Learning
This method provides an efficient preprocessing approach for diverse machine learning tasks, offering memory savings and privacy protection, but it is incremental as it applies an existing transform to new data.
The paper tackled the problem of data preprocessing for machine learning by using the Bloom filter transform to encode samples into compact, privacy-preserving bit arrays, resulting in models achieving similar accuracy to those trained on raw data while reducing memory use and enhancing privacy across six datasets.
We present a method that uses the Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact, privacy-preserving bit array. This reduces memory use and protects the original data while keeping enough structure for accurate classification. We test the method on six datasets: SMS Spam Collection, ECG200, Adult 50K, CDC Diabetes, MNIST, and Fashion MNIST. Four classifiers are used: Extreme Gradient Boosting, Deep Neural Networks, Convolutional Neural Networks, and Logistic Regression. Results show that models trained on Bloom filter encodings achieve accuracy similar to models trained on raw data or other transforms. At the same time, the method provides memory savings while enhancing privacy. These results suggest that the Bloom filter transform is an efficient preprocessing approach for diverse machine learning tasks.