Acquaintance Digest Authentication Using Contact List Entropy
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Solution Overview
Problem
Existing password-based authentication methods hinder security while promoting cryptographically better passwords, making them cumbersome for everyday use and vulnerable to attacks.
Innovation Solution
A method that identifies users by extracting and normalizing entropy from their interpersonal relationship information stored in datastores, such as digital address books or online profiles, to generate a unique 'acquaintance digest' or fingerprint, which can be used to authenticate access to protected resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional password-based authentication is used, then security can be maintained through cryptographic strength, but user convenience deteriorates due to cumbersome password management
Solution Approach 1:
The system automatically generates authentication credentials from the user's existing contact list data without requiring manual password creation or management. The credential is derived self-service style from data the user already maintains for other purposes, eliminating the need for separate password management while maintaining security
Solution Approach 2:
The system transforms static password parameters into dynamic credentials that automatically change when the underlying contact list data changes. This parameter transformation converts a manually managed static secret into a dynamically regenerated authentication value based on evolving interpersonal relationship data
2Object-affected harmful factors
If passwords are made more secure through length and complexity requirements, then resistance to attacks improves, but ease of operation worsens due to harder-to-remember passwords
Solution Approach 1:
The system replaces the mechanical approach of human memorization with an automated computational system that generates and manages credentials algorithmically. Instead of relying on human memory for complex passwords, a software agent automatically derives credentials from contact list data using cryptographic functions
Solution Approach 2:
The contact list data serves as an intermediary between the user's personal information and the authentication system. Rather than directly using passwords, the system uses the contact list as a intermediary data source that is transformed into authentication credentials through hashing and cryptographic processing
3Reliability
If password aging is implemented to preclude compromised password use, then security improves over time, but loss of time increases due to regular password changes
Solution Approach 1:
The authentication credential is made dynamic rather than static, automatically updating whenever the underlying contact list data changes. This dynamic regeneration eliminates the need for periodic manual password changes while ensuring that compromised credentials become invalid when the contact list is updated
Solution Approach 2:
The system implements periodic regeneration of authentication credentials based on changes in the contact list data rather than on a fixed time schedule. This event-driven periodic action ensures credentials are refreshed when needed (when contact data changes) without imposing unnecessary regular updates
4Reliability
If unique identifiers are assigned to users for authentication, then access control improves, but device complexity increases due to password verification systems
Solution Approach 1:
The system extracts the authentication credential directly from the user's contact list data using cryptographic hashing, removing the need for separate password storage and verification infrastructure. The credential is extracted on-demand from existing data rather than being stored separately, simplifying the authentication system architecture
Data Source
AI summary
According to this disclosure, a user is identified (and selectively granted access to protected resources) by using information that describes the user's interpersonal relationships. This information typically is stored in a datastore, such as a digital address book, an online profile page, or the like. The user's digital address book carries an “acquaintance pattern” that changes dynamically in time. This pattern comprises the information in the user's contact list entries. In this approach, the entropy inherent in this information is distilled into a unique acquaintance digest (or “fingerprint”) by normalizing the contact list data, and then applying a cryptographic function to the result.