Privacy Wrapper Negotiation for Pervasive Data Access Control
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Solution Overview
Problem
The challenge lies in effectively managing data privacy and security in pervasive computing environments where personal and private information is collected and replicated across vast networks, making it difficult to protect, delete, and secure due to redundancy and misuse by unauthorized parties.
Innovation Solution
A security and privacy wrapper is implemented to negotiate the usage and dispensation of data, ensuring that only necessary information is shared and stored, with features like data tagging, masking, and expiration modules to control data access and usage, preventing unauthorized use and misuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected in bulk across vast networks for various purposes, then data availability and analytical capability are improved, but data security and privacy protection deteriorate due to redundancy and unauthorized access
Solution Approach 1:
The patent segments data into two distinct categories: essential data required for the primary transaction purpose and extraneous data collected incidentally. This segmentation enables differential privacy protection strategies, where extraneous data can be anonymized, masked, or deleted while essential data maintains its integrity and accessibility for the intended purpose.
Solution Approach 2:
The patent extracts extraneous data from the bulk collected data set and applies specific privacy protection measures to it. By separating extraneous data from essential data, the system can apply targeted anonymization, masking, or deletion techniques to the extraneous portion while preserving the utility of essential data for the primary transaction purpose.
2Productivity
If copies of data are replicated across datacenters and the computing cloud, then data accessibility and processing capability are improved, but data protection and deletion become difficult due to redundancy
Solution Approach 1:
The patent applies different quality levels of privacy protection to different portions of data based on their classification. Essential data maintains full quality and accessibility for authorized processing, while extraneous data receives enhanced privacy protection through anonymization, masking, or deletion. This local quality differentiation enables high productivity for essential data processing while ensuring strong protection for extraneous data across distributed systems.
3Quantity of substance
If extraneous data is collected along with necessary transaction data, then comprehensive data gathering is improved, but unauthorized usage and misuse increase
Solution Approach 1:
The patent extracts extraneous data from the collected data set and applies specific privacy protection measures to it. By separating extraneous data from essential data, the system can apply targeted anonymization, masking, or deletion techniques to the extraneous portion while preserving the utility of essential data for the primary transaction purpose.
Solution Approach 2:
The patent changes the privacy protection parameters applied to different data categories. Essential data maintains standard accessibility and usability parameters, while extraneous data has its parameters modified through anonymization (removing identifying characteristics), masking (obscuring sensitive information), or deletion (complete removal). These parameter changes prevent unauthorized usage while preserving necessary data utility.
Data Source
AI summary
A method to provide negotiation control to data such that a person or entity can negotiate the use of data gathered beyond what is needed for a particular use by a third party transaction.