Probabilistic Data Structures for Password Recovery
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Solution Overview
Problem
Traditional methods for recovering sensitive data, such as passwords from hash values, are time- and cost-inefficient, requiring significant processing time and computing resources, making them impractical for environments with limited resources and portable devices.
Innovation Solution
The use of probabilistic data structures, like Bloom filters, to segment the password space into sets and generate corresponding digest sets, allowing for faster and more efficient search by querying probabilistic data structures to identify potential matches, reducing the need for exhaustive comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional brute-force or dictionary-based methods are used for password recovery, then complete password space coverage is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the complete password space into multiple segments or partitions. Each segment is processed independently using probabilistic data structures, allowing parallel processing and reducing the time required to search the entire password space while maintaining complete coverage through systematic segmentation.
Solution Approach 2:
The patent pre-computes and stores probabilistic data structures (such as Bloom filters) for different password segments before actual password recovery is needed. This preliminary action enables rapid querying during recovery operations without performing exhaustive searches in real-time, significantly reducing processing time.
2Reliability
If traditional exhaustive search methods are used, then all possible passwords are checked, but computational resources and memory storage requirements become prohibitive
Solution Approach 1:
The patent introduces probabilistic data structures as intermediary representations of password segments. These structures act as mediators between the query hash and the actual password space, enabling efficient filtering and reducing the need for exhaustive comparisons while maintaining acceptable search accuracy through probabilistic matching.
Solution Approach 2:
The patent creates compressed copies of password segment information using probabilistic data structures instead of storing complete password lists. These compact representations consume significantly less memory and computational resources while preserving the essential information needed for effective password recovery.
3Reliability
If exhaustive password space searching is performed, then no passwords are missed, but the method becomes impractical for portable devices with limited resources
Solution Approach 1:
The patent segments the password space and uses probabilistic data structures to enable distributed and portable processing. Each segment can be processed independently on resource-constrained devices, allowing password recovery operations to be performed on portable systems without requiring exhaustive search capabilities.
Solution Approach 2:
The patent changes the parameters of the search approach by using probabilistic data structures with configurable false positive rates. This allows tuning the balance between recovery success rate and resource consumption, making the system adaptable to portable devices with limited computational resources while maintaining acceptable reliability.
Data Source
AI summary
Systems and methods for recovering passwords from a hash value input are provided. A password space may be segmented into password sets, and a digest set may be generated for each password set. Probabilistic data structures representing the digest sets may be generated. One of the probabilistic data structures may be queried with the hash value input to determine whether the hash value input is likely included in the digest sets. In response to the hash value input being determined to be likely included in the digest set, the passwords constituting the password set corresponding to the digest set may be regenerated, and the hash values constituting the digest set may be regenerated. The generated hash values may be compared to the hash value input to determine a hash value from the digest set that matches the hash value input to recover the password associated with the matched hash value.