Private Session Recommendations With Local Data Anonymization
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Solution Overview
Problem
Existing data analytics systems face challenges in providing personalized recommendations while ensuring user privacy, as anonymizing user responses consumes resources and may compromise data security, and non-anonymized data collection risks privacy breaches.
Innovation Solution
Implementing a private session using a restricted browsing environment that locally stores user data and interactions, enabling personalized resource access without permanent storage, and anonymizing data for external sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user data is anonymized for external sharing, then user privacy is protected, but data processing resources and time are consumed
Solution Approach 1:
The system segments data handling into two distinct paths: local private browsing data storage and anonymized external data sharing. The private browsing component handles user interactions locally without external communication, while the data sharing component processes anonymized data for research purposes. This segmentation allows privacy protection without requiring anonymization of all user data, reducing processing overhead.
Solution Approach 2:
The system extracts and removes personally identifiable information from user data before external sharing, creating anonymized datasets. By extracting only the necessary anonymized features rather than processing complete user profiles, the system reduces the computational resources required for data processing while maintaining privacy protection.
2Adaptability or versatility
If user data is collected without anonymization, then personalized recommendations can be provided, but user privacy may be compromised
Solution Approach 1:
The system introduces an intermediary anonymization layer between user data collection and external sharing. This intermediary process removes personally identifiable information while preserving the structural patterns needed for personalized recommendations. The anonymized data serves as a mediator that enables both privacy protection and recommendation personalization.
Solution Approach 2:
The system applies different quality standards to different data handling contexts: full user data is maintained locally for personalized recommendations, while only anonymized data is shared externally. This local quality approach allows the system to maintain high data fidelity where needed (for personalization) while ensuring privacy protection where data leaves the user's control.
3Ease of operation
If browsing data is stored permanently, then user preferences can be remembered, but security risks increase
Solution Approach 1:
The system employs temporary, disposable browsing data storage that automatically expires or can be easily deleted. User browsing preferences are stored in volatile memory or temporary storage areas that do not persist indefinitely. This approach maintains ease of operation during active use while minimizing security risks associated with long-term data retention.
Data Source
AI summary
In some implementations, a device may obtain, via a navigation platform, data indicating a first one or more responses to a questionnaire. The device may establish, based on obtaining the data, a private session associated with managing the data. The device may determine, based on the data, one or more recommended resources, from a set of resources, that indicate relevant content based on the first one or more responses. The device may provide, via the private session, the one or more recommended resources. The device may obtain, via the private session, interaction information associated with one or more interactions with the one or more recommended resources that occur via the navigation platform. The device may anonymize the data and the interaction information to generate response data that is in response to the questionnaire. The device may provide, to a server device, the response data.