Bloom Filter Entry Removal via Iterative Hashing and Segmentation
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Solution Overview
Problem
Bloom filters cannot efficiently add or remove elements while maintaining memory efficiency, as altering bits to remove an entry can impact other entries, compromising the integrity of the filter, and using counting filters increases size and memory usage.
Innovation Solution
Implementing an iterative process using an iteration value to add or remove entries from Bloom filters by hashing the entry and iteration value, allowing for the toggling of bits to indicate presence or absence, and utilizing binary search for efficient query optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If entries are removed from a Bloom filter by changing bits from one to zero, then the ability to remove entries is improved, but the integrity of the Bloom filter is compromised as it may impact other entries
Solution Approach 1:
The Bloom filter is segmented into multiple independent bit arrays, where each bit array can be modified independently. This segmentation allows removal of entries from one segment without affecting other segments, resolving the contradiction between entry removal capability and filter integrity.
2Ease of operation
If counting filters with n-bit counters are used to remove entries, then the ability to remove entries is improved, but memory efficiency is sacrificed as the size of the Bloom filter increases
Solution Approach 1:
Different bit arrays within the Bloom filter have different properties: some are read-only for memory efficiency, while others are writable for entry removal. This local differentiation allows the system to achieve entry removal capability in specific segments without increasing the memory usage of the entire filter structure.
Data Source
AI summary
A computer system stores a probabilistic data structure generated based at least in part on a plurality of digital signatures generated based at least in part on a plurality of components of a data set. The computer system receives a digital signature for verification. An entry can verify the digital signature within the probabilistic data structure by verifying the digital signature against the probabilistic data structure.