Privacy-Driven Data Sharing via Autoencoder Architecture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users are hesitant to share data online due to privacy concerns, leading to difficulties for online providers in personalizing interactions and complying with regulations like GDPR, which can result in inefficient operations and reduced user trust.
Innovation Solution
A method and system for privacy-driven data sharing that computes a benefit-to-resource score to select an autoencoder architecture, transforming data to minimize reconstruction loss and storage space, and storing it in a user-controlled space, allowing for personalized interactions while preserving user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user data is collected and stored for personalized interactions, then personalization capability is improved, but user privacy protection deteriorates
Solution Approach 1:
The patent introduces a trusted intermediary system that acts as a mediator between users and service providers. This intermediary computes benefit-to-resource scores and manages data transformation without revealing raw personal data to service providers, thereby enabling personalization while protecting user privacy through the intermediary's controlled data handling.
Solution Approach 2:
The patent transforms data from its original form into a different representation (transformed data) that preserves utility for personalization while removing personally identifiable information. This parameter change in data representation allows the system to maintain adaptability for personalized interactions while reducing privacy risks through mathematical transformation.
2Quantity of substance
If data is transformed to minimize storage space, then resource efficiency is improved, but data quality for personalization deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming data into a compressed representation that retains essential patterns and characteristics needed for personalization. The transformation function modifies data parameters to reduce storage requirements while preserving the informational content necessary for generating personalized interactions.
Solution Approach 2:
The patent creates transformed copies of the original data that serve the same functional purpose for personalization but occupy less storage space. These copied transformed data representations maintain the essential relationships and patterns needed for user personalization while reducing the quantity of stored information.
3Reliability
If strict data privacy measures are implemented, then user trust is improved, but operational efficiency deteriorates
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically computes benefit-to-resource scores and applies appropriate transformation functions without requiring manual intervention. This automated self-service approach maintains strong privacy protections through consistent application of transformation rules while preserving operational efficiency by eliminating manual data handling steps.
Solution Approach 2:
The patent changes the operational parameters of data handling by applying automated transformation functions that convert raw data to protected representations in real-time. This parameter change enables the system to maintain high user trust through consistent privacy protection while preserving operational efficiency through automated, rule-based data processing.
Data Source
AI summary
A processor may be configured to perform operations that include computing a benefit-to-resource score for a dataset and selecting an autoencoder architecture based on the benefit-to-resource score. The autoencoder architecture may balance minimizing reconstruction loss with minimizing required storage space based on the benefit-to-resource score. The operations performed by the processor may further include transforming the dataset into transformed data with a transformation function based on the autoencoder architecture and storing the transformed data in a user space.