Point-Cloud Privacy via Linear Transformation in Collaborative Mapping
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Solution Overview
Problem
The privacy of collaborative mapping in AR/MR applications is compromised due to the potential misuse of merged structural information from multiple mobile communications devices, as existing encryption methods hinder data merging and continuous updates.
Innovation Solution
A mobile communications device applies a current linear transformation, shared among a group of trusted devices, to derive a concealed representation of point-cloud data, which is then transmitted to a map server for merging with a concealed point-cloud map, ensuring privacy without obstructing collaborative mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If encryption is applied to protect privacy of point-cloud data, then privacy protection is improved, but data merging capability deteriorates
Solution Approach 1:
The patent introduces a linear transformation matrix as an intermediary mechanism between the point-cloud data and the server. This matrix acts as a key that allows the data to be concealed from third parties while still enabling legitimate merging operations. The transformation matrix serves as a mediator that preserves both privacy and data utility without requiring full encryption that would block merging.
Solution Approach 2:
The patent changes the parameters of the point-cloud data by applying linear transformations (rotation, scaling, shearing) to the spatial coordinates. This parameter transformation conceals the original structural information from unauthorized access while maintaining the relative geometric relationships needed for collaborative mapping. The transformed data appears different to third parties but remains usable for legitimate merging operations.
2Object-affected harmful factors
If point-cloud data is concealed to prevent analytics by third parties, then privacy protection is improved, but collaborative mapping updates deteriorate
Solution Approach 1:
The patent segments the collaborative mapping system into distinct components: local devices that generate and transform point-cloud data, a server that merges concealed data, and trusted third parties that access only concealed representations. This segmentation allows each component to operate with appropriate privacy constraints while maintaining overall system functionality. The linear transformation acts as a boundary that separates unauthorized analytics from legitimate merging operations.
Solution Approach 2:
The patent creates a transformed copy of the point-cloud data through linear transformation. This copy preserves the essential geometric relationships and structural information needed for collaborative mapping while appearing different to unauthorized observers. The transformed copy can be merged with other transformed data from different devices without revealing the original untransformed structures to third parties.
Data Source
AI summary
A mobile communications device for performing localization and mapping is disclosed, comprising a sensor operative to capture sensor data of its local environment, and operative to derive point-cloud data from the sensor data, which represents structural features of the local environment, determine a pose of the mobile communications device relative to a point-cloud map representing the local environment, derive a concealed representation of the point-cloud data, by applying a current linear transformation to the point-cloud data, and transmit the concealed representation of the point-cloud data to a map server. The current linear transformation is a shared secret, or is derivable from a shared secret, which shared secret is available to a group of mobile communications devices comprising the mobile communications device. Also disclosed are the map sever and a trusted server.