Adaptive Trust Profile Generation with Privacy Preservation
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Solution Overview
Problem
Existing systems fail to effectively mitigate security risks associated with entities whose behavior may change due to internal or external factors, such as financial pressure or radicalization, leading to potential malicious actions, and lack adaptive measures to reassess and respond to evolving risk profiles.
Innovation Solution
A method and system that monitor electronically-observable actions of entities, convert these actions into electronic information, and generate adaptive trust profiles that are privacy-enhanced, allowing for real-time risk assessment and responsive security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entity behavior is monitored and trust profiles are generated to mitigate security risks, then security reliability is improved, but entity privacy is compromised
Solution Approach 1:
The patent introduces privacy-enhancing technologies as intermediaries between entity behavior monitoring and trust profile generation. These intermediaries process behavior data through techniques such as differential privacy, homomorphic encryption, or federated learning, allowing the system to generate accurate trust profiles while preserving entity privacy. The intermediary layer transforms raw behavior data into privacy-preserving representations that maintain security assessment capability without exposing sensitive entity information.
Solution Approach 2:
The patent changes the parameters of data representation by transforming entity behavior data into privacy-enhanced formats. Instead of storing or processing raw entity information, the system transforms data into mathematical representations where privacy is preserved through parameter transformations such as adding noise to satisfy differential privacy constraints, using homomorphic encryption to maintain data in encrypted form during processing, or converting behavior patterns into aggregate statistics that lose individual identifiability while retaining analytical value for trust assessment.
2Measurement precision
If continuous monitoring of entity actions is performed to detect behavioral changes, then risk detection capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the continuous monitoring system into modular components: behavior data collection modules, privacy-enhancing transformation modules, trust profile generation modules, and risk assessment modules. Each segment performs a specific function and can be independently configured, deployed, and maintained. This segmentation allows the system to achieve high measurement precision through coordinated operation of specialized modules while managing complexity through clear separation of concerns and standardized interfaces between modules.
Solution Approach 2:
The patent applies preliminary actions by pre-configuring privacy-enhancing transformation parameters and trust profile structures before monitoring begins. The system pre-establishes the mathematical frameworks for privacy preservation (such as differential privacy budgets or encryption schemes) and pre-defines trust profile schemas. This preliminary configuration reduces runtime complexity and allows the continuous monitoring to focus on data collection and assessment rather than decision-making about how to process data.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for generating an adaptive trust profile, comprising: monitoring an electronically-observable action of an entity, the electronically-observable action of the entity corresponding to an event enacted by the entity; converting the electronically-observable action of the entity to electronic information representing the action of the entity; and generating the adaptive trust profile based upon the action of the entity, the adaptive trust profile being privacy enhanced.