Zero-Trust Algorithm Deployment via Sequestered Nodes
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Solution Overview
Problem
The challenge lies in securely processing sensitive data with proprietary algorithms in a zero-trust environment, where data stewards and algorithm developers need to maintain secrecy while overcoming the barriers of large dataset transfer times and regulatory compliance, particularly in healthcare where protected health information is involved.
Innovation Solution
The system employs encrypted algorithms and data processing within sequestered computing nodes using public-private key techniques, allowing secure operation and report generation without exposing the data or algorithms to unauthorized parties, with the core management system handling encryption, key management, and data harmonization to facilitate secure data processing across diverse infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is transferred from data steward to algorithm developer for processing, then algorithm execution is enabled, but transfer time increases significantly due to large dataset size
Solution Approach 1:
A trusted third-party computing environment is introduced as an intermediary between the data steward and algorithm developer. The data remains at the steward's location while the algorithm is executed in the trusted environment with controlled data access, eliminating the need for direct data transfer and reducing transfer time to near zero.
Solution Approach 2:
The system separates the data from the algorithm execution environment. Data remains segmented and isolated at the data steward's infrastructure, while only necessary data subsets are accessed in the trusted computing environment through secure interfaces, minimizing data movement while enabling algorithm processing.
2Productivity
If proprietary algorithms are shared with data stewards for local execution, then processing speed improves, but algorithm secrecy and intellectual property protection deteriorate
Solution Approach 1:
A trusted third-party computing environment serves as an intermediary that hosts the proprietary algorithms securely. The algorithm developer retains control over algorithm access and execution, while the data steward benefits from local processing capability through the intermediary's infrastructure, maintaining both speed and secrecy.
Solution Approach 2:
The trusted computing environment provides localized secure execution capabilities at the data steward's infrastructure. This allows algorithm execution to occur locally without requiring full algorithm disclosure, as the secure environment enforces access controls and prevents unauthorized copying or extraction of the proprietary code.
3Adaptability or versatility
If sensitive data is shared with algorithm developers for processing, then algorithm training and execution are enabled, but data security and regulatory compliance deteriorate
Solution Approach 1:
The trusted third-party computing environment acts as an intermediary that enables data processing while enforcing security controls. Data remains at the steward's infrastructure and is only accessed through the secure environment with controlled permissions, enabling algorithm execution while maintaining data security and regulatory compliance through the intermediary's security framework.
Solution Approach 2:
The system implements localized data access controls within the trusted computing environment. Different data subsets are made accessible to different algorithm execution contexts based on security policies, allowing versatile data processing while maintaining granular security controls and compliance with regulations through the intermediary's enforcement mechanisms.
4Reliability
If data is kept secret at data steward infrastructure, then data security is maintained, but algorithm execution capability deteriorates due to inability to share data
Solution Approach 1:
The trusted third-party computing environment serves as an intermediary that bridges the gap between data security requirements and algorithm execution needs. It provides a secure interface that allows algorithms to access and process data while the data remains protected at the steward's infrastructure, maintaining both security and execution capability through the intermediary's secure access controls.
Data Source
AI summary
Systems and methods for the deployment and operation of an algorithm in a zero-trust environment are provided. In some embodiments, an algorithm is encrypted by an algorithm developer within a zero-trust computing node, using a public key. This generates a payload that is transferred to a core management system which in turn distributes the payload to one or more sequestered computing nodes located within the infrastructure of one or more data stewards. The sequestered computing nodes are designed to preserve privacy of data assets and the algorithm. Next the payloads are decrypted, using a private key, within the sequestered computing nodes. This yields the algorithm that can be run against the data assets of the data steward. A report is generated that can be shared with the appropriate parties.