Bloom Filter Authentication List for Risk Engine Memory Optimization
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Solution Overview
Problem
Conventional authorization systems face high memory consumption and long access times due to large EFN lists, which can lead to costly increases in risk engine components to manage cache memory effectively.
Innovation Solution
Implementing a Bloom filtered authentication list, where a risk engine device applies a Bloom filter algorithm to a master authentication list, generating a significantly smaller list that decouples memory consumption, allowing for efficient comparison and minimizing data access times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authorization systems cache all EFN list entries in memory, then complete fraud detection coverage is achieved, but cache memory consumption becomes excessively high
Solution Approach 1:
The patent creates a Bloom filter data structure that serves as a compressed copy of the EFN list. Instead of storing complete authentication entries, the system generates hash values from the original list and stores only these hash values in the Bloom filter, dramatically reducing memory consumption while maintaining fraud detection capability through probabilistic matching
Solution Approach 2:
The system transforms the authentication data from its original format into hash values, changing the parameter representation from full authentication records to compressed hash signatures. This parameter transformation enables the same fraud detection function with minimal memory footprint
2Reliability
If conventional systems increase the number of risk engine components to manage large EFN lists, then fraud detection capability is maintained, but system cost increases
Solution Approach 1:
The Bloom filter transforms the data representation into a compact hash-based structure that can be efficiently processed by a single risk engine component, eliminating the need for multiple components and reducing system complexity and cost
3Reliability
If conventional authorization systems store large EFN lists in cache memory, then comprehensive authentication checking is possible, but data access time increases
Solution Approach 1:
The Bloom filter creates a lightweight copy of the authentication data using hash values that can be rapidly compared against incoming authentication requests, dramatically reducing data access time while maintaining comprehensive checking capability through the probabilistic nature of hash matching
Data Source
AI summary
A risk engine device of an enterprise's authentication system is configured to apply a Bloom filter algorithm to a master authentication list from a database to generate the Bloom filtered authentication list. Once generated, the risk engine device stores the Bloom filtered authentication list as part of an associated cache memory. Because the Bloom filtered authentication list is smaller in size relative to the master authentication list, by utilizing the Bloom filtered authentication list, the risk engine device decouples the linear dependency between the master authentication list and cache memory consumption, thereby reducing the relatively excessive cache memory use found in conventional risk engines.