Perturbation Seed Generation via Hashed Metrics
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Solution Overview
Problem
Existing data perturbation methods are not strong enough to prevent security breaches, as they can be susceptible to consecutive request attacks and require significant processing resources and time, especially as datasets grow, leading to increased costs.
Innovation Solution
A computing system generates a perturbation seed by hashing rounded metrics calculated from datapoints using a secure hash algorithm like SHA-3, which is used to perturb statistics without significantly increasing processing time or resources, ensuring consistent and secure data obfuscation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing data perturbation methods are used, then data can be obfuscated, but they are susceptible to consecutive request attacks and require significant processing resources and time
Solution Approach 1:
The patent applies preliminary action by pre-computing a perturbation seed from the dataset characteristics (such as dataset size, feature dimensions, or hash values of data samples) before actual data perturbation occurs. This pre-computed seed is then reused across multiple consecutive requests, eliminating the need to re-generate perturbation parameters for each request. The seed generation happens once in advance, and subsequent perturbations use this fixed seed, thereby preventing consecutive request attacks while avoiding repeated computational overhead.
Solution Approach 2:
The patent uses copying by creating a derived perturbation seed that represents the essential characteristics of the dataset without copying the actual sensitive data. This seed is a compact representation (such as a hash value or aggregated metric) that can be repeatedly used to generate perturbed versions of the data. Instead of processing the full dataset multiple times, the system copies only the essential statistical properties into the seed, which then drives all subsequent perturbation operations.
2Reliability
If strong data perturbation is applied to prevent security breaches, then sensitive information is protected, but processing resources and time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-computing a perturbation seed from the dataset characteristics (such as dataset size, feature dimensions, or hash values of data samples) before actual data perturbation occurs. This pre-computed seed is then reused across multiple consecutive requests, eliminating the need to re-generate perturbation parameters for each request. The seed generation happens once in advance, and subsequent perturbations use this fixed seed, thereby preventing consecutive request attacks while avoiding repeated computational overhead.
Solution Approach 2:
The patent applies parameter changes by transforming the perturbation approach from using fixed random seeds or simple noise parameters to using dynamically generated seeds based on dataset characteristics. The seed is derived from parameters such as dataset size, feature dimensions, or hash values, which are inherent properties of the data itself. This parameter-based seed generation ensures that the perturbation strength and pattern are appropriately scaled to the data characteristics while maintaining computational efficiency through reuse.
3Reliability
If data perturbation is used to protect sensitive information, then individual data points are obfuscated, but the ability to derive individual information from aggregated statistics remains
Solution Approach 1:
The patent applies preliminary action by pre-computing a perturbation seed from the dataset characteristics (such as dataset size, feature dimensions, or hash values of data samples) before actual data perturbation occurs. This pre-computed seed is then reused across multiple consecutive requests, eliminating the need to re-generate perturbation parameters for each request. The seed generation happens once in advance, and subsequent perturbations use this fixed seed, thereby preventing consecutive request attacks while avoiding repeated computational overhead.
Solution Approach 2:
The patent uses an intermediary approach by introducing a perturbation seed as a mediator between the original sensitive data and the released aggregated statistics. The seed is derived from dataset characteristics but does not directly reveal individual data points. It serves as an intermediate parameter that controls the perturbation process, adding noise or distortion to aggregate statistics in a way that prevents derivation of individual information while preserving overall data utility for analysis.
Data Source
AI summary
Described herein are example implementations for generating a perturbation seed for the perturbation of electronic data. A system obtains a plurality of datapoints (with one or more statistics calculated from the plurality of datapoints to be perturbed based on a perturbation seed). The system calculates one or more metrics from the plurality of datapoints. The system also generates, for each of the one or more metrics, a rounded metric by rounding the metric. The system further generates the perturbation seed. Generating the perturbation seed includes hashing the one or more rounded metrics. Rounding a metric may be to a defined place value (such as the second most significant place value), and a binary output of hashing the one or more rounded metrics may be converted to a number. The system may perturb one or more statistics based on the perturbation seed and output the one or more perturbed statistics.