Deriving Individual Attributes from Anonymous Aggregate Data
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Solution Overview
Problem
Businesses face challenges in obtaining relevant customer-specific data due to privacy policies and laws that restrict the dissemination of personally identifiable information, making it difficult to use aggregate data effectively for marketing purposes.
Innovation Solution
A computer-implemented method that derives individual attributes from anonymous aggregate data by identifying aggregation keys, mapping inputs to these keys, and generating attributes based on distribution sets, allowing for the use of data without storing or releasing personally identifiable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personally identifiable information (PII) is collected and stored for marketing purposes, then the accuracy and relevance of customer-specific data improves, but privacy violations and legal compliance issues worsen
Solution Approach 1:
The patent extracts and removes personally identifiable information from the data collection process. Instead of storing PII, the system uses only anonymized aggregate data that cannot be traced back to individual consumers, thereby eliminating privacy violations while maintaining marketing effectiveness through statistical analysis of population groups.
Solution Approach 2:
The patent introduces an intermediary layer of aggregation and anonymization between data collection and marketing analysis. By processing data through aggregation keys and statistical models that work with population-level data rather than individual records, the system mediates between the need for accurate customer insights and the requirement to protect individual privacy.
2Object-affected harmful factors
If aggregate data is used for marketing purposes, then data privacy is protected, but the accuracy and usefulness of individual attribute derivation worsens
Solution Approach 1:
The patent changes the parameters of data analysis from individual-level attributes to aggregate statistical parameters. By working with distribution sets, mean values, standard deviations, and other statistical measures of population groups rather than individual records, the system maintains both privacy protection and analytical accuracy through statistical inference.
Solution Approach 2:
The patent segments the population into distinct groups using aggregation keys (such as demographic categories, geographic regions, or behavioral segments). By analyzing and deriving attributes for these segments rather than attempting to analyze individuals directly, the system achieves accurate marketing insights while maintaining privacy through the anonymity of aggregate data.
3Productivity
If personally identifiable information is required for marketing effectiveness, then marketing precision improves, but data availability and compliance with privacy policies worsen
Solution Approach 1:
The patent creates statistical copies or proxies for individual-level data through aggregate analysis. Instead of requiring actual PII, the system generates synthetic representations of customer attributes based on statistical patterns in population data, enabling marketing effectiveness through these statistical copies while avoiding the need for sensitive personal information.
Data Source
AI summary
An apparatus and method to increase accuracy in individual attributes derived from anonymous aggregate data uses aggregation keys in order to retrieve distribution sets and generate best-effort results for individual attributes. Multiple aggregation keys may be utilized to which individual attributes may be cross-mapped. The aggregation keys may be divided into location-based aggregation keys and name-based aggregation keys. The resulting data may be of varying granularity depending upon the granularity of the aggregation key used for the distribution and to generate the attributes.