Anonymized Visitor Tracking via Salted MAC Hashing
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Solution Overview
Problem
Proximity Recognition Systems (PRS) face challenges in securing visitor MAC addresses while maintaining anonymization and segmenting visitor behavior data, particularly in environments where correlation of visitor traffic patterns across different venues is undesirable.
Innovation Solution
The system generates a unique 'salt' for each Visitor Correlation Group (VCG), combining it with MAC addresses from received packets and cryptographically hashing them to create anonymized identifiers, ensuring that readings from the same device across PRDs within a VCG are mapped to the same identifier, while different VCGs remain uncorrelated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MAC addresses are used to identify visitors in PRS, then visitor recognition and tracking capability is improved, but visitor privacy and anonymity are compromised
Solution Approach 1:
The patent introduces a salt value as an intermediary element between the MAC address and the visitor identifier. The salt is combined with the MAC address through cryptographic hashing to produce a anonymized visitor identifier. This intermediary mechanism allows the system to recognize visitors based on their MAC addresses while simultaneously protecting their privacy by preventing direct association between the MAC address and the identifier used for tracking and analysis.
Solution Approach 2:
The patent transforms the MAC address parameter through cryptographic hashing and combination with a salt value, changing its form from a direct identifier to an anonymized identifier. This parameter transformation maintains the ability to recognize and track visitors while altering the fundamental property of the identifier to protect privacy. The hashed value cannot be reversed to obtain the original MAC address, thus changing the reversibility parameter of the identification system.
2Adaptability or versatility
If visitor data is collected across multiple venues, then comprehensive visitor behavior analysis is improved, but correlation of visitor behavior between venues becomes possible
Solution Approach 1:
The patent segments the visitor identification system into venue-specific salt values. Each venue maintains its own unique salt value that is used to hash MAC addresses into visitor identifiers. This segmentation ensures that visitor identifiers generated at different venues are based on different salt values, making it impossible to correlate visitor behavior across venues while still enabling comprehensive analysis within each individual venue.
3Object-affected harmful factors
If MAC addresses are anonymized through hashing, then visitor anonymity is improved, but ability to correlate readings from same device across PRDs is lost
Solution Approach 1:
The patent makes the salt value serve multiple functions: it acts as both a privacy protection mechanism through cryptographic hashing and as a correlation key for identifying the same device across different PRDs within the same venue. The salt is combined with the MAC address to produce a visitor identifier that can be consistently used across all PRDs in the venue, enabling both anonymity and device correlation.
Data Source
AI summary
A method and system for the anonymization and segmentation of the media access control (MAC) addresses reported by visitors' 802.11 enabled devices at a venue operator premises. This system assures a venue operator and its visitors that no individually identifying information about a visitor is re-transmitted or stored that can be traced back to their MAC address, while still allowing the venue operator to obtain venue visitor counts visit frequencies well as traffic patterns during visits (i.e. dwell times at, and movements between, locations within a venue) and counts of common visitors between venues. It also ensures that the data generated is segmented so that the data obtained by two different venue operators is not correlatable between visitors common to the two sets of data.