PPK-means Clustering Using Hamming Vectors for Privacy
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Solution Overview
Problem
The standard K-means clustering algorithm is inefficient for large datasets, consuming significant resources and time, especially when dealing with millions of data points in high-dimensional spaces, and lacks privacy preservation mechanisms for secure data processing.
Innovation Solution
A Privacy-Preserving K-means (PPK-means) algorithm that encodes real-valued vectors into bit vectors, utilizing Hamming distance and Locality Sensitive Hashing (LSH) to facilitate fast and approximate clustering while maintaining privacy by only processing encoded data, and estimating centroid signatures probabilistically using a Gaussian mixture model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard K-means clustering algorithm is used on large datasets, then clustering accuracy is maintained, but processing time and memory usage increase significantly
Solution Approach 1:
The patent uses approximate centroid signatures instead of exact centroids, and employs bit vector encodings that can be quickly computed and discarded. The algorithm computes approximate centroids using probabilistic methods (e.g., random projection, sampling) rather than maintaining precise centroid values throughout the clustering process, thereby reducing computational overhead while preserving acceptable clustering accuracy.
Solution Approach 2:
The patent transforms the data representation by encoding real-valued vectors into bit vectors (e.g., using locality-sensitive hashing or random projection). This parameter change from continuous to discrete representation enables faster Hamming distance computations and reduces memory requirements, while the encoding schemes are designed to preserve the essential clustering structure.
2Measurement precision
If standard K-means clustering algorithm is used on large datasets, then clustering accuracy is maintained, but memory consumption increases significantly
Solution Approach 1:
The patent uses compact bit vector representations for data points and approximate centroid signatures, which consume significantly less memory than storing full high-dimensional real-valued vectors. The algorithm processes these compressed representations throughout clustering iterations, eliminating the need to maintain large in-memory copies of the original dataset.
Solution Approach 2:
The patent applies dimensionality reduction techniques by projecting high-dimensional data into lower-dimensional spaces (e.g., using random projection or LSH) and encoding them as bit vectors. This parameter transformation reduces the memory footprint from O(N×D) to O(N×m) where m << D, while preserving the essential geometric relationships needed for clustering.
3Productivity
If encoded bit vectors are used for clustering, then processing speed is improved, but data privacy is compromised
Solution Approach 1:
The patent introduces encoded bit vectors as an intermediary representation between the original sensitive data and the clustering algorithm. The encoding schemes (e.g., locality-sensitive hashing, random projection) transform the data in a way that preserves clustering structure but makes it computationally infeasible to reconstruct the original data from the encoded representations, thereby providing privacy protection while enabling fast processing.
Data Source
AI summary
This method is a process that improves the execution time and maintains very precise clustering effectiveness utilizing a unique algorithm (identified as PPK means) that optimizes a process that is referred to as K-means clustering. The PPK means algorithm utilizes estimation values of signatures of new centroids for speed improvement and encoded data to provide a level of privacy protection. A system comprises a processor, operably coupled to memory, and that executes the computer-executable components, wherein the computer-executable components comprise: an encoding component that encodes a set of real-time valued vectors as bit vectors; and a clustering component that performs K-means clustering on the bit encoded vectors.