Privacy Enabled Task Allocation System
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Solution Overview
Problem
Conventional methods for task allocation in service-based organizations focus on user satisfaction and revenue maximization, neglecting data privacy and relying on static roles, which leads to repeated tasks by the same agents, increasing the risk of data breaches.
Innovation Solution
A method and system for privacy-enabled task allocation that dynamically classifies tasks based on attributes, identifies competent agents, calculates data exposure scores, and allocates tasks considering trust values, risk attributes, and conflict matrices to ensure data privacy, using a dynamic data analysis unit and predefined allocation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static roles are used for task allocation, then ease of operation is improved, but data privacy security deteriorates due to repeated tasks by the same agents
Solution Approach 1:
The patent implements dynamic task allocation by transitioning from static roles to a system that continuously evaluates agent trust values, task risk attributes, and data exposure scores. The allocation parameters are dynamically updated based on agent behavior patterns and task sensitivity, ensuring that no single agent repeatedly handles the same category of tasks, thereby maintaining data privacy security while preserving operational ease.
2Reliability
If dynamic task classification and allocation is implemented, then data privacy security is improved, but device complexity increases
Solution Approach 1:
The patent segments the task allocation system into distinct functional modules: task classification module, agent competency identification module, trust value calculation module, risk attribute assessment module, and allocation decision module. Each module handles a specific aspect of the allocation process, making the complex system manageable and maintainable while achieving improved data privacy security through coordinated operation of these specialized components.
3Reliability
If multiple allocation parameters including data exposure score are considered, then data privacy security is improved, but productivity decreases due to increased calculation overhead
Solution Approach 1:
The patent performs preliminary calculations of trust values, risk attributes, and data exposure scores during task classification and agent evaluation phases, before the actual allocation decision is made. This allows the allocation algorithm to use pre-computed values rather than calculating everything in real-time, reducing the computational overhead during the critical allocation moment while maintaining comprehensive privacy security checks.
Data Source
AI summary
Data is an asset to any organization and any breach to the data during task allocation to agents may lead to serious damage to organizations including loss of consumer confidence, trust, reputation, financial penalties and the like. Conventional methods mainly focus on the allocating task to agents based on user satisfaction, overall throughput and maximize revenue and less focus is given to data privacy. The present subject matter overcomes the limitations of the conventional methods for task allocation by utilizing a dynamic data exposure analysis method, which enables seamless upgrading of the data access policy and or control. Here, a data exposure is monitored based on a data exposure score, dynamic identification of conflicting tasks and a dynamic privacy budget. The data exposure score is calculated in two execution points. Finally all the values are updated in the system for utilization in the further privacy enabled task allocation.