Bloom Filter Encoding for Machine Learning

2025-12-22

AI Summary

This paper addresses three key challenges in machine learning data preprocessing: high memory overhead, significant privacy leakage risk, and difficulty preserving structural information. To tackle these, we propose a novel Bloom filter–based encoding method that maps raw samples into compact, irreversible binary vectors. This work is the first to systematically validate Bloom filters as a general-purpose, privacy-enhancing feature transformation technique. Empirical evaluation across six heterogeneous datasets—using XGBoost, DNNs, CNNs, and logistic regression—demonstrates classification accuracy comparable to that achieved on raw data (average degradation <1.2%), while reducing memory consumption by up to 87%. Crucially, the original features are provably unrecoverable, thereby eliminating reconstruction-based privacy threats. Our core contribution lies in establishing both the theoretical applicability and empirical superiority of Bloom filters for lightweight, privacy-preserving preprocessing.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Data Compression
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• Encodes data into privacy-preserving bit arrays
• Reduces memory usage while maintaining classification accuracy
• Provides efficient preprocessing for diverse machine learning tasks

Innovation

• Bloom filter transform encodes data into bit arrays
• Method reduces memory use and enhances privacy
• Achieves similar accuracy to raw data models