Secured Virtual Container Processing for Private Hyper-Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users are reluctant to share personal data for hyper-personalized experiences due to concerns about data misuse and security, while service providers hesitate to distribute proprietary machine learning models for fear of misappropriation.
Innovation Solution
A secured virtual container isolates user data from the operating system, enabling hyper-personalization by processing user data within a protected environment, allowing secure storage and application of machine learning models without exposing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users provide private data to service providers for hyper-personalized experiences, then personalization accuracy is improved, but data security and privacy protection deteriorate
Solution Approach 1:
The system segments the data processing function by creating a separate secured virtual container that isolates user data from the operating system. This container houses the machine learning model and processes data independently, preventing direct access by OS processes while enabling personalized experiences. The segmentation resolves the contradiction by allowing data processing for personalization without exposing data to potential security breaches in the main OS environment.
Solution Approach 2:
The secured virtual container acts as an intermediary between user data and the operating system. It receives data from the OS, processes it through the machine learning model for personalization, and returns results without allowing the OS to access the raw user data. This intermediary structure enables personalization accuracy while maintaining data security by preventing direct OS access to sensitive information.
2Adaptability or versatility
If service providers distribute proprietary machine learning models, then hyper-personalization capability is improved, but model security and proprietary information protection deteriorate
Solution Approach 1:
The machine learning model is extracted from the service provider's central system and deployed within the secured virtual container on the user's device. This extraction allows the model to function locally without being distributed across multiple systems where it could be accessed or misappropriated. The model remains proprietary and secure within the isolated container while enabling hyper-personalization capabilities on the user's device.
Solution Approach 2:
Instead of distributing the proprietary model across multiple systems, the system creates a secure copy of the model within the virtual container. This copy enables the model to function for personalization purposes without exposing the original proprietary information. The copying approach allows hyper-personalization capability while maintaining model security by keeping the model enclosed in the isolated container rather than widely distributed.
3Ease of operation
If users trust service providers with private data, then hyper-personalized experience is improved, but user privacy control and data ownership deteriorate
Solution Approach 1:
The system enables self-service by allowing the user's device to process and own the personalization logic through the secured virtual container. The machine learning model executes locally within the container using user data, eliminating the need for users to trust external service providers with their data. The device serves itself by processing data locally for personalization while maintaining full user privacy control and data ownership.
Solution Approach 2:
Instead of requiring users to provide data to service providers for personalization, the system inverts the approach by having the service provider's model execute within the user's secured environment. This inversion transfers the trust requirement from the user to the service provider, allowing users to maintain privacy control while still receiving hyper-personalized experiences through locally-executed model processing.
Data Source
AI summary
A secured virtual container is enabled to securely store personal data corresponding to a user, where such data is inaccessible to processes running outside the secured virtual container. The secured virtual container may also include an execution environment for a machine learning model where the model is securely stored and inaccessible. Personal data may be feature engineered and provided to the machine learning model for training purposes and/or to generate inference values corresponding to the user data. Inference values may thereafter be relayed by a broker application from the secured virtual container to applications external to the container. Applications may perform hyper-personalization operations based at least in part on received inference values. The broker application may enable external applications to subscribe to notifications regarding availability of inference values. The broker may also provide inference values in response to a query.