Geospatial-Temporal Pathogen Exposure Tracing Through zkSNARKs
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Solution Overview
Problem
Existing contact tracing methods face challenges in balancing privacy concerns with the need for effective and timely identification of pathogen exposure, particularly in large populations, and do not adequately address transitive exposure risks.
Innovation Solution
A decentralized, privacy-preserving contact tracing system using zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs) generates cryptographic proofs of proximity and exposure, which are verified through a machine learning model to provide actionable intelligence on pathogen exposure risks, including transitive risks, without revealing personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional contact tracing methods are used to identify individuals in contact with infected persons, then pathogen exposure can be identified, but personal privacy information is revealed
Solution Approach 1:
The patent introduces cryptographic intermediaries (proximity tokens, hashes, and zkSNARK proofs) that mediate between contact tracing functionality and privacy protection. These cryptographic elements enable exposure identification without directly exposing personal information, as the system verifies proximity relationships through mathematical proofs rather than sharing raw data.
Solution Approach 2:
The patent extracts only the essential contact tracing functionality (proximity verification and exposure identification) from the broader context of personal data sharing. By using zkSNARKs, the system extracts and verifies only the necessary proof elements (whether contact occurred, timing, location) while leaving personal identifiers and sensitive information extracted and hidden from the verification process.
2Measurement precision
If manual contact tracing is performed to ensure accurate identification of contacts, then exposure risks are identified, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system implements self-service contact tracing where mobile devices automatically perform proximity detection, token exchange, and cryptographic proof generation without human intervention. The devices autonomously manage the entire contact tracing workflow including generating proximity tokens, exchanging them with nearby devices, computing hashes, and creating zkSNARK proofs, eliminating the need for manual data collection and processing.
Solution Approach 2:
The patent replaces manual mechanical contact tracing processes with automated cryptographic systems. Instead of health workers manually interviewing and recording contact information, the system uses mobile devices with cryptographic libraries to automatically perform proximity verification through token exchange and hash computation, substituting human labor with automated computational processes.
3Loss of information
If only first-order contacts are traced to protect privacy, then personal information is protected, but transitive exposure risks are missed
Solution Approach 1:
The patent extends contact tracing from a single dimension (direct contacts only) to multiple dimensions by enabling transitive exposure verification. Through composability of zkSNARK proofs, the system can verify not only direct contacts but also contacts of contacts (second-order, third-order, etc.), creating a multi-layered verification structure that captures transitive exposure risks while maintaining privacy through cryptographic abstraction.
Solution Approach 2:
The system performs preliminary cryptographic setup and proof generation that enables flexible verification of exposure chains of any length. By pre-computing proximity proofs and making them composable, the system can later verify transitive exposures without requiring real-time computation or additional privacy compromises, as the cryptographic structure already supports multi-hop verification.
4Measurement precision
If centralized contact tracing systems are used to collect comprehensive data, then exposure identification is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the contact tracing system into independent modular components: proximity token generation, hash computation, zkSNARK proof creation, and verification. Each component operates independently and can be implemented as separate software modules or hardware functions. This segmentation allows the system to achieve comprehensive exposure detection through composition of simple, well-defined units rather than a complex centralized system.
Solution Approach 2:
The system uses cryptographic copying where proximity relationships are represented by reproducible cryptographic artifacts (tokens, hashes, proofs) rather than storing actual personal data. These cryptographic copies can be verified without accessing the original sensitive information, enabling comprehensive exposure tracking through verified copies while maintaining privacy and reducing system complexity.
Data Source
AI summary
Techniques for geospatial-temporal pathogen tracing in zero knowledge include: generating, by a first user device, a first proximity token for contact tracing; receiving, by the first user device, a second proximity token from a second user device; generating, by the first user device, a hash based on the first proximity token and the second proximity token; generating, by the first user device using a prover function of a preprocessing zero knowledge succinct non-interactive argument of knowledge (pp-zk-SNARK), a cryptographic proof attesting that an individual associated with the first user device tested positive for a pathogen; transmitting, by the first user device, first publicly verifiable exposure data including at least the cryptographic proof and the hash to a public registry; and applying at least the first publicly verifiable exposure data and second publicly verifiable exposure data to a machine learning model, to obtain actionable intelligence associated with the pathogen.