Localization Offloading via Trusted Execution Environment and MPC
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Solution Overview
Problem
Existing localization technologies face challenges in efficiently processing high computational resource requirements, especially in resource-constrained devices and complex environments, while also ensuring privacy preservation and security against advanced adversaries.
Innovation Solution
The method involves offloading localization processing from devices to network resources using privacy-preserving techniques like flexible Multi-Party Computation (MPC) and split device network resources. This approach allows for the identification of break conditions during iterative localization algorithms, enabling early termination and secure data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If obfuscation techniques are used to protect privacy in localization, then device resource consumption is reduced, but security against advanced adversaries is compromised
Solution Approach 1:
The patent introduces a trusted execution environment (TEE) as an intermediary component that mediates between the localization processing and the network. The TEE creates a secure enclave that protects sensitive data and computational processes, allowing the system to use obfuscation techniques for resource efficiency while maintaining security through the TEE's cryptographic protections and isolated execution environment.
2Productivity
If computational tasks are offloaded to cloud/edge servers, then processing efficiency is improved, but privacy preservation becomes more difficult
Solution Approach 1:
The patent segments the localization processing into multiple components: device-side preprocessing, TEE-protected computation, and cloud/edge-assisted processing. This segmentation allows different privacy and security requirements to be applied to different parts of the system, enabling efficient cloud processing while maintaining privacy through selective encryption and secure enclaves at critical stages.
Solution Approach 2:
The trusted execution environment serves as an intermediary layer between the device and cloud/edge servers, providing a secure bridge that enables efficient remote processing while protecting privacy. The TEE verifies data integrity and protects sensitive information during transmission and processing, allowing the system to leverage cloud computing resources without compromising privacy.
3Reliability
If homomorphic encryption is used for privacy-preserving localization, then information-theoretic security is achieved, but computational overhead becomes unrealistically high
Solution Approach 1:
The patent applies different security mechanisms to different parts of the system based on local requirements. Homomorphic encryption or other strong cryptographic techniques are applied only where absolutely necessary (in the TEE and for specific data transmissions), while less computationally intensive protection methods are used in other areas, achieving security where needed without system-wide computational overhead.
Solution Approach 2:
The system dynamically adjusts security parameters and cryptographic strength based on the specific processing context, data sensitivity, and computational constraints. This allows the system to achieve information-theoretic security for critical operations while using more efficient cryptographic approaches for less sensitive tasks, optimizing the balance between security and computational overhead.
4Loss of information
If line cloud obfuscation is used to protect environmental features, then reconstruction of original features is prevented, but geometric features can still be recovered by resource-abundant adversaries
Solution Approach 1:
The trusted execution environment acts as a secure intermediary that processes localization data with enhanced protection against sophisticated attacks. The TEE implements additional security measures beyond standard obfuscation, including secure key management and protected computational paths, making it significantly more difficult for resource-abundant adversaries to recover geometric features from obfuscated line clouds.
Data Source
AI summary
A method (1100) by a server (120) is provided for improving the performance of iterative localization algorithms run using privacy preserving techniques. The method begins when the server receives (1105), from a device 105, information associated with an environment of the device. Based on the information associated with the environment of the device, the server runs (1110) a localization algorithm using privacy preserving techniques. Prior to iterative steps in the localization algorithm reaching a maximum number of iterations, the server identifies (1115) at least one break condition released intermittently when running the localization algorithm. Based on the at least one break condition identified while running the localization algorithm, the server ceases (1120) the localization algorithm before the maximum number of iterations has been reached and transmits a localization result to the device.