Prevalence Estimation Using Query Data Hashing
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Solution Overview
Problem
Current data security systems face challenges in efficiently processing large volumes of data to provide real-time reputation scores, especially when dealing with distributed architectures and high volumes of queries, which requires significant computational and storage resources.
Innovation Solution
A system that estimates prevalence information using query data, employing statistical models to calculate the number of unique devices querying for an object, allowing for real-time processing with reduced computational and storage needs by using a hash function to generate sequences of bits for comparison, thereby maintaining a maximum number of matching bits without counting all unique device IDs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system counts all unique device IDs to calculate prevalence, then measurement precision is improved, but device complexity and computational resources increase significantly
Solution Approach 1:
The patent uses temporary, inexpensive data structures (such as hash tables with limited retention) to track device prevalence. Instead of maintaining permanent records of all unique device IDs, the system uses ephemeral tracking mechanisms that are discarded after serving their purpose, significantly reducing storage requirements while maintaining measurement capability.
Solution Approach 2:
The system transforms the tracking approach from counting unique device IDs (discrete, high-cardinality parameter) to measuring prevalence through aggregated statistical parameters. By changing the measurement parameter from exact device counts to prevalence estimates based on sampling or aggregation, the system reduces computational complexity while maintaining useful measurement precision.
2Productivity
If the system processes large volumes of query data in real-time, then productivity is improved, but use of energy and computational resources increase
Solution Approach 1:
The system applies partial action by processing only the necessary subset of query data in real-time rather than analyzing every single query in full detail. It uses sampling techniques and incremental updates to maintain prevalence information with reduced computational effort, achieving real-time responsiveness without processing the entire data volume exhaustively.
Solution Approach 2:
The prevalence tracking system is designed to be self-updating through automated counter increments and statistical calculations. Once initialized, the system automatically maintains prevalence counts through simple arithmetic operations on incoming queries without requiring complex real-time analysis, reducing the energy burden of continuous processing.
3Reliability
If the system maintains detailed records of all queries, then reliability is improved, but loss of information increases due to data churn
Solution Approach 1:
The system extracts only the essential information needed for prevalence measurement from the full query data. Instead of maintaining complete records of all queries including redundant details, it extracts and retains only the critical elements (such as device identifiers and prevalence-relevant metadata), reducing data churn while preserving the reliability needed for accurate prevalence calculation.
Data Source
AI summary
In one example, a data security system may determine prevalence of a file based query data for an object (e.g., a file or a hash or a file). An example algorithm may provide using a statistically justifiable estimate of the prevalence while storing few data records, and therefore may provide prevalence information in O(1) time complexity (i.e., constant time). Such an algorithm may be applied in near real-time to provide, e.g., an immediate response to a query for the prevalence of a file.