Bloom Filter Compression Using Slepian-Wolf Side Information
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for transmitting Bloom filters over a network are inefficient in terms of bandwidth usage, especially when the transmitter and receiver units have similar but not identical Bloom filters, as they do not leverage side information for compression.
Innovation Solution
Utilizing Slepian-Wolf encoding techniques, specifically distributed source encoding, to compress Bloom filters by encoding them using a syndrome-based Low Density Parity Check Code, allowing the receiver to decompress using side information from its local Bloom filter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Bloom filters are transmitted as complete messages via data networks, then data integrity is maintained, but bandwidth consumption increases significantly
Solution Approach 1:
The Bloom filter transmission is segmented into two parts: (1) a compressed representation using Slepian-Wolf encoding that captures the essential information, and (2) side information from the receiver's local Bloom filter that complements the transmitted data. This segmentation allows the transmitter to send only the differential changes rather than the complete Bloom filter, reducing bandwidth while maintaining reconstruction accuracy.
Solution Approach 2:
The receiver pre-computes and stores a local Bloom filter that is similar to the one it expects to receive. This preliminary action creates side information that can be used during the decompression phase to reconstruct the transmitted Bloom filter from the compressed message, eliminating the need to transmit redundant data and reducing bandwidth consumption.
2Loss of energy
If Slepian-Wolf encoding is used to compress Bloom filters, then bandwidth is reduced by up to 50 times, but the decoding complexity increases
Solution Approach 1:
The decoding process leverages the receiver's own local Bloom filter as side information to assist in reconstructing the transmitted Bloom filter. This self-service approach allows the receiver to use its existing data structures and computational resources to perform the decompression, reducing the need for complex external decoding algorithms and minimizing the increase in decoding complexity.
3Measurement precision
If the Bloom filter size is increased to reduce false-positive probability, then query accuracy improves, but the compression ratio decreases
Solution Approach 1:
The invention changes the parameter being transmitted from the complete Bloom filter state to the differential information required to transform the receiver's local Bloom filter into the transmitter's Bloom filter. This parameter change allows the system to maintain high query accuracy with larger Bloom filters while achieving significant compression ratios, because only the changes between similar Bloom filters need to be transmitted.
Data Source
AI summary
The invention relates to a method for transmitting a Bloom filter from a transmitter unit to a receiver unit. The method includes the following steps of providing a Bloom filter by the transmitter unit; compressing the Bloom filter by the transmitter unit, the compression of the Bloom filter being performed based on a Slepian-Wolf encoding method; and transmitting the Bloom filter compressed using the Slepian-Wolf encoding method from the transmitter unit to the receiver unit.