Proof of Possession Verification via Pre-Session Inference
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Solution Overview
Problem
Existing technologies face challenges in efficiently proving user ownership or control over network endpoints, leading to latency and potential fraud in transactions that require proof of possession, such as content subscriptions.
Innovation Solution
A system that provides proof of possession by using a combination of deterministic and heuristic methods, including one-time passwords and geolocation signals, to verify user control over endpoints, while also inferring user intent to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional proof of possession methods are used, then user ownership verification is achieved, but latency increases and user interruption risk rises
Solution Approach 1:
The system performs proof of possession verification in advance during the HTTP session before the actual content subscription transaction occurs. By pre-verifying user control over the endpoint using deterministic methods (one-time passwords) and heuristic methods (geolocation signals, device identifiers) during the browsing session, the system eliminates the need for time-consuming verification steps at the moment of transaction, thus reducing latency while maintaining reliable verification.
Solution Approach 2:
The system introduces an intermediary verification mechanism that operates within the HTTP session framework. Instead of direct real-time verification at the transaction moment, the system uses session-based intermediaries (cookies, session tokens, pre-exchanged authentication data) to carry verification information, enabling faster completion of the proof of possession process without compromising security or reliability.
2Loss of time
If real-time verification is performed, then latency is reduced, but system complexity increases
Solution Approach 1:
The verification system is segmented into two independent modules: deterministic verification (using one-time passwords and cryptographic authentication) and heuristic verification (using geolocation signals, device identifiers, and behavioral analysis). Each module operates independently with its own protocols and data requirements, allowing the system to select appropriate verification methods based on context without requiring a monolithic complex system. This modular segmentation reduces overall system complexity while enabling real-time verification capabilities.
Solution Approach 2:
The system dynamically adjusts verification parameters based on the HTTP session context, user behavior patterns, and risk assessment. Rather than using a fixed complex verification protocol, the system changes verification parameters (such as requiring one-time password vs. accepting device identifiers, adjusting geolocation precision requirements) to match the specific transaction context, reducing complexity while maintaining real-time verification efficiency.
3Reliability
If multiple verification methods are used, then verification reliability improves, but processing overhead increases
Solution Approach 1:
The system implements partial verification by selecting only the necessary verification methods based on the HTTP session context and transaction type. Rather than always applying all available verification methods (deterministic one-time passwords, heuristic geolocation, device identifiers), the system applies only the appropriate subset needed for each specific transaction, reducing computing overhead while maintaining sufficient verification reliability through contextual risk assessment.
Solution Approach 2:
The system uses feedback mechanisms to optimize verification resource usage. By monitoring verification outcomes, transaction risk levels, and session patterns, the system dynamically adjusts which verification methods to apply in subsequent transactions. This feedback-driven approach ensures that multiple verification methods are used only when necessary, improving reliability when needed while minimizing computing overhead through learned optimization from previous verification experiences.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for mitigating computing latency associated with proving of possession of network endpoints. One system includes (i) a user device, e.g., a smartphone or a personal computer, (ii) a proof of possession service running on one or more computers, and (iii) an authorization service running on one or more computers.