Cross-Device User Identification via Hashed PII Matching
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Solution Overview
Problem
Online systems face challenges in accurately identifying and tracking user actions performed on external web pages when users are not logged into the system, leading to incomplete social graph understanding and underestimated content performance.
Innovation Solution
The solution involves using third-party system-provided personally identifiable information (PII) to match users across different devices and browsers, hashing this information for privacy, and sending it to the online system for user activity synchronization, allowing the online system to create cookies and update user profiles even when users are logged out.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the online system uses cookie-based user identification, then user tracking is simple and reliable when users are logged in, but user identification fails when users are not logged in or use different devices/browsers
Solution Approach 1:
The patent introduces PII (personally identifiable information) as an intermediary element that bridges the gap between different identification systems. When users are not logged in, the system collects PII (such as email addresses, phone numbers) from partner systems, hashes this information, and uses it as a mediator to identify users across different devices and browsers. This intermediary mechanism allows the system to maintain reliable user identification while adapting to scenarios where traditional cookie-based identification fails.
Solution Approach 2:
The system dynamically changes the identification parameter based on user login state. When users are logged in, the system uses cookie-based user IDs for identification. When users are not logged in or use new devices, the system switches to using hashed PII as the identification parameter. This parameter change strategy ensures both reliability (using the most accurate identifier available) and adaptability (switching between different identification methods based on context).
2Measurement precision
If the online system collects and processes PII for user matching, then cross-device user identification accuracy improves, but system complexity and data privacy concerns increase
Solution Approach 1:
The system performs preliminary hashing of PII data at the partner system level before transmitting it to the online system. This preliminary action reduces the complexity of the online system's data processing requirements, as the hashing computation is already completed. It also addresses privacy concerns by ensuring that sensitive PII is transformed into hashed form before being stored or processed by the online system, reducing the risk associated with handling raw personal data.
Solution Approach 2:
The system creates hashed copies of PII data rather than storing or processing the original sensitive information. This copying approach maintains measurement precision (the hashed form can still be used for accurate user matching) while reducing system complexity related to data security and privacy management. The hashed copies serve as surrogate identifiers that preserve the ability to match user actions across devices without requiring the system to manage complex security protocols for raw PII.
3Loss of information
If the online system implements comprehensive user tracking across all devices, then content performance measurement improves, but user privacy protection becomes more difficult
Solution Approach 1:
The patent converts the potential harm of PII collection into a benefit by immediately hashing the PII data. The hashing process transforms sensitive personal information into an irreversible coded form, which can be used for accurate user tracking (completing user behavior information) while inherently protecting user privacy. The very act of collecting PII, which could be harmful, is converted into a beneficial process by applying hashing, thus achieving both comprehensive tracking and privacy protection simultaneously.
Solution Approach 2:
The system replaces direct storage and processing of raw PII with a cryptographic hashing mechanism. Instead of using mechanical or direct storage methods that expose sensitive information, the system substitutes this with a mathematical transformation process that preserves the ability to track user behavior while eliminating privacy risks. This substitution allows the system to maintain complete user behavior information for accurate content performance measurement without the harmful factor of raw PII exposure.
Data Source
AI summary
Embodiments include one or more client devices accessible by users, an online system, and one or more partner systems such that the online system is able to identify a user of the online system across different devices and browsers based on the user activity that occurs external to the online system. A user performs user actions (e.g. purchase a product) on a web page of a partner system and may provide personally identifiable information (PII) to the partner system. The partner system provides the hashed PII and user actions performed by the user to the online system. The online system identifies a user profile on the online system by matching personal information in the user profile to the hashed PII. The online system generates a confidence score indicating a likelihood that the identified user of the online system is the individual that performed the external user action.