Localized Privacy Engine for On-Device Personalization
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Solution Overview
Problem
Current personalized mobile applications often require users to share sensitive data with third parties for cloud-based processing, leading to data security concerns and unauthorized data access, while also lacking efficient local data collection and accurate user preference inference.
Innovation Solution
A localized privacy engine is integrated into mobile devices to monitor user interactions and create personalized user profiles locally, managing sensitive information exposure only to applications on the device and using personas to approximate user interests for personalization without transmitting sensitive data to third parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user data is aggregated in the cloud for large-scale data mining, then personalization accuracy is improved, but data security and user privacy are compromised
Solution Approach 1:
The patent introduces a localized privacy engine as an intermediary component that sits between user data and applications. This engine processes and anonymizes user data locally on the device before making it available to applications, preventing raw sensitive data from being exposed while still enabling personalization. The privacy engine acts as a mediator that allows data utility for personalization while blocking direct access to sensitive information.
Solution Approach 2:
The patent segments the personalization system into multiple components: a profiling service that collects data, a localized privacy engine that processes and anonymizes data, and a profile exposure component that manages data access. This segmentation allows different functions to be performed in isolated zones, with the privacy engine ensuring that even if one component is compromised, the overall system maintains security through localized processing.
2Adaptability or versatility
If sensitive user information is exposed to third parties for cloud processing, then personalization capabilities are enhanced, but unauthorized data access increases
Solution Approach 1:
The localized privacy engine serves as a trusted intermediary that enables personalization capabilities without requiring direct exposure of sensitive data to third parties or applications. It processes user interactions and creates anonymized profiles that can be used for personalization while maintaining a security barrier that prevents unauthorized access to the actual sensitive information.
Solution Approach 2:
The system creates anonymized copies of user data through the profiling service and privacy engine. These copies contain sufficient information for personalization purposes but have sensitive identifiers removed or transformed. Applications receive and process these anonymized copies rather than the original sensitive data, enabling personalization while preserving security.
3Object-affected harmful factors
If user interactions are monitored locally for personalization, then data security is improved, but personalization accuracy may be reduced
Solution Approach 1:
The patent replaces cloud-based mechanical processing systems with localized processing on the user device. The profiling service and privacy engine run locally, monitoring user interactions and creating personalization profiles without requiring data transmission to external servers. This substitution maintains security by keeping data on-device while using sophisticated local algorithms to ensure personalization accuracy.
Solution Approach 2:
The privacy engine transforms user data parameters through anonymization and aggregation processes. It changes the form of data from identifiable individual records to aggregated, anonymized patterns that preserve statistical properties needed for accurate personalization inference while removing security risks associated with identifiable information.
4Object-affected harmful factors
If a localized privacy engine is implemented on the device, then user privacy is protected, but device complexity increases
Solution Approach 1:
The patent merges the profiling service, privacy processing logic, and profile exposure management into a single integrated localized privacy engine component. This consolidation reduces the number of separate system components and interfaces that would otherwise be needed, managing device complexity while maintaining comprehensive privacy protection functionality.
Solution Approach 2:
The localized privacy engine is designed as a universal component that performs multiple functions: monitoring user interactions, processing and anonymizing data, managing profile exposure to applications, and enforcing privacy policies. This multi-functionality reduces the need for separate specialized components, managing overall system complexity while providing comprehensive privacy protection.
Data Source
AI summary
A profiling service may determine, local to a device, user profile attributes associated with a device user based on interaction of the device user with the device, based on device-local monitoring of device user interactions with the device, and may store the user profile attributes in a memory. The profiling service may be configured as an augmentation to a device operating system of the device. A profile exposure component may manage exposure of information associated with the user profile attributes to applications operating locally on the device, without exposure to the applications or to third parties of information determined as sensitive to the device user.