Privacy Enhanced Proximity Tracker Using Homomorphic Encryption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional proximity tracking systems fail to accurately track users while protecting their private location data, leading to either exposure of sensitive information or poor accuracy due to unenrolled users creating blind spots.
Innovation Solution
Implementing double-blind collaborative proximity tracking using homomorphic encryption, where user identity and location information are divided among multiple parties, allowing computations to determine proximity without exposing unencrypted data, ensuring privacy and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional proximity tracking systems acquire users' personal location information to improve tracking accuracy, then tracking accuracy is improved, but user privacy is compromised
Solution Approach 1:
The system divides user data into two separate components: location information stored by the location detector and user identifiers held by the proximity tracker. This segmentation ensures that no single entity possesses both components, preventing privacy exposure while maintaining tracking accuracy through secure collaboration.
Solution Approach 2:
Homomorphic encryption serves as an intermediary mechanism that enables the proximity tracker to perform proximity computations on encrypted location data without decrypting it. The encryption layer acts as a mediator that preserves privacy while allowing accurate proximity measurements to be computed and returned.
2Reliability
If users waive their privacy to allow proximity tracking, then tracking coverage is improved, but security protection is reduced
Solution Approach 1:
By segmenting data ownership so that the location detector holds encrypted location data and the proximity tracker holds user identifiers, the system enables broad user participation (improved coverage) while maintaining security through distributed data storage. Users can enroll without exposing their data to a single trusted entity.
Solution Approach 2:
Homomorphic encryption creates an inert computational environment where proximity computations can be performed on encrypted data without exposing the underlying information. This allows users to participate in tracking with enhanced security, as their data remains encrypted throughout the entire processing pipeline.
3Object-affected harmful factors
If users do not share their location information to protect privacy, then security is improved, but tracking accuracy deteriorates due to blind spots
Solution Approach 1:
Homomorphic encryption acts as an intermediary that enables users to contribute their location data to the tracking system without exposing it. The encryption layer mediates between the user's privacy concerns and the system's need for accurate location information, allowing users to participate while maintaining privacy protection.
Solution Approach 2:
The system changes the state of location data from plaintext to homomorphically encrypted form, transforming it into a state that can be processed for proximity measurements while inherently protecting privacy. This parameter change (encryption state) enables both privacy protection and tracking accuracy to coexist.
4Device complexity
If a single party possesses both unencrypted user identifiers and location information, then proximity computation is simplified, but data security is compromised
Solution Approach 1:
The system deliberately segments data possession across two separate parties: the location detector holds encrypted location information and the proximity tracker holds unencrypted user identifiers. This segmentation increases system complexity but eliminates the security risk of a single party possessing both data types, as neither party can independently expose user location information.
Solution Approach 2:
Homomorphic encryption serves as an intermediary computational framework that enables proximity computations to be performed across the data segmentation boundary. The encryption system mediates the interaction between the two parties, allowing the proximity tracker to compute results without directly accessing encrypted location data, thus maintaining security while achieving the computational goal.
Data Source
AI summary
A device, system and method for privacy enhanced proximity detection by secure collaboration between a first party without access to user locations and a second party without access to a target user identifier. The second party may receive from the first party a homomorphic encryption public key and homomorphic encrypted target user identifier or masked target location, and may determine an associated homomorphic encrypted target user location. The second party may search a homomorphically encrypt database of user locations and associated user identifiers for homomorphic encrypted proximate user identifiers associated with homomorphic encrypted user locations proximate to the homomorphic encrypted target user location. The second party may send the first user the search result of homomorphic encrypted proximate user identifiers to be decrypted by the first party with a private key to identify proximate user identifiers without knowing their locations.