IoT Device Compliance Validation Using Tree Data Structures
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Solution Overview
Problem
The proliferation of IoT devices has led to security concerns due to their computational and power constraints, making them vulnerable to network risks, especially when their transient nature requires access to multiple networks.
Innovation Solution
A method using tree data structures, specifically Merkle trees and bloom trees, to monitor and validate compliance of device attributes with network security policies by comparing compliance data with attribute data associated with electronic devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic security methods are implemented on IoT devices, then security is improved, but computational cost and power consumption increase
Solution Approach 1:
The patent segments the security validation process into two parts: lightweight attribute extraction and compliance checking on IoT devices, and comprehensive verification using tree data structures on network operators. This divides the computationally expensive cryptographic operations from the constrained devices to the more capable network infrastructure, resolving the contradiction between security and power consumption.
Solution Approach 2:
The patent introduces tree data structures (Merkle trees, Bloom trees) as an intermediary mechanism between IoT devices and network operators. These structures enable efficient compliance validation without requiring full cryptographic verification on every device, thus maintaining security while reducing computational and power demands on IoT devices.
2Measurement precision
If comprehensive device attribute verification is performed, then compliance accuracy is improved, but validation time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing device attributes in tree data structures before actual compliance validation. The tree structures (particularly Bloom trees) are prepared in advance with hashed attributes, enabling rapid lookup and comparison during validation without performing comprehensive checks in real-time, thus maintaining accuracy while reducing validation time.
Solution Approach 2:
The patent uses Bloom filters within the tree structures that provide probabilistic rather than deterministic membership testing. This partial action approach allows for faster validation by accepting a small probability of false positives, trading absolute compliance accuracy for significantly reduced validation time in high-throughput scenarios.
3Measurement precision
If detailed device attributes are stored and compared, then compliance monitoring accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent creates simplified copies of device attributes in the form of hashed representations within tree data structures. Instead of storing and comparing full device attribute details, the system uses hash copies (Merkle hashes, Bloom filter bits) that preserve compliance information while dramatically reducing data complexity and processing requirements.
Solution Approach 2:
The patent replaces complex mechanical comparison operations with mathematical hash functions and probabilistic data structure operations. Tree-based hash comparisons and Bloom filter membership tests substitute for intricate attribute-by-attribute verification, reducing computational complexity while maintaining monitoring accuracy.
Data Source
AI summary
A method for monitoring or validating device compliance of the attributes of a device on a network, the method including providing a first tree data structure including compliance data associated with a network; performing a comparison of the first tree data structure with a second tree data structure comprising attribute data associated with an electronic device to compare the compliance data with the attribute data; and determining, based on the comparison, whether the electronic device is compliant with the network.