Context Information Management Using Random Keys and Sparse Tables
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Solution Overview
Problem
Existing database management techniques for context information face challenges in efficiently updating and querying while maintaining user privacy, leading to suboptimal performance due to frequent database collisions and complex access rules.
Innovation Solution
Implementing a sparse table with large random index values and using a binary search algorithm for context information management, along with a trusted execution environment for secure data transmission, to de-identify and de-correlate user data, ensuring efficient updates and queries while preserving privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional database structure is used to store context information, then data can be easily accessed and queried, but user privacy cannot be protected because database consumers can identify users and determine relationships between data values
Solution Approach 1:
The patent introduces random keys as an intermediary between user identities and context information. Instead of directly storing or accessing data using user identifiers, the system uses random keys that act as mediators - they allow data retrieval for authorized users while preventing identification of the actual users by database consumers. This intermediary layer resolves the contradiction by enabling data access functionality while blocking privacy-violating operations.
2Reliability
If context information is de-identified to protect privacy, then user anonymity is maintained, but it becomes difficult to provide personalized services and targeted advertising
Solution Approach 1:
The patent implements local quality by providing different levels of data identification to different entities. For authorized users (like the user themselves or service providers with permission), the data maintains local identifiability through associated random keys that can retrieve specific user information. For general database consumers, the data appears de-identified and anonymous. This localized differentiation of data quality resolves the contradiction between anonymity and personalized service capability.
3Productivity
If frequent updates and queries are performed on context information database, then the database remains current and useful, but collision processing overhead increases and performance degrades
Solution Approach 1:
The patent applies parameter changes by transforming the indexing parameter from traditional user identifiers to large random key values. This parameter change fundamentally alters the collision characteristics of the database operations. Large random keys distribute data more uniformly across the database space, significantly reducing collision frequency during updates and queries. This resolves the contradiction by changing the fundamental parameter (indexing key) to one that minimizes collision processing overhead while maintaining operational efficiency.
Data Source
AI summary
Improved techniques for context information management are described. In one embodiment, for example, an apparatus may comprise a processor element, a selection component for execution by the processor element to select a context information item, a random number generator to generate a random number, and an anonymization component for execution by the processor element to generate a random key based on the random number and send a message associating the context information item with the random key. Other embodiments are described and claimed.