Privacy-Preserving Distributed IoT Attestation via Homomorphic Encryption
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Solution Overview
Problem
Internet of Things (IoT) devices face challenges in minimizing security threats while protecting user privacy during attestation processes, as thorough attestations can lead to privacy loss and potential security risks.
Innovation Solution
The implementation of additive homomorphic encryption (AHE) and a MESH reporting algorithm, combined with a Trusted Execution Environment (TEE) and The Onion Routing (TOR) network, allows IoT devices to aggregate telemetry data anonymously, ensuring that individual devices remain untraceable, and only high-risk data is reported to a trusted network manager, while using a M of N threshold scheme to protect against rogue nodes and man-in-the-middle attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thorough attestation is performed to verify device security, then security reliability is improved, but user privacy is compromised
Solution Approach 1:
The system segments the attestation process into multiple independent reporting nodes that each verify specific security attributes. Instead of a single centralized attestation authority that collects all device information, multiple nodes perform distributed verification of security-critical functions, allowing security validation without centralized privacy intrusion
Solution Approach 2:
The patent introduces trusted reporting nodes as intermediaries between devices and the external verifier. These nodes act as mediators that can verify device security attributes and generate attestation reports without the external verifier directly accessing or learning about the device's private information, thus preserving user privacy while maintaining security verification
2Measurement precision
If device identity information is collected for attestation verification, then security verification accuracy is improved, but device anonymity is reduced
Solution Approach 1:
The system uses cryptographic copies (attestation reports) of device security attributes instead of collecting actual device identity information. The reporting nodes generate verified copies of security-critical data that can be examined for verification purposes without revealing the device's true identity or sensitive information
Solution Approach 2:
The patent applies different quality levels of information disclosure to different verification needs. Sensitive identity information remains locally protected and undisclosed, while only specific security-critical attributes are verified and reported. This selective disclosure maintains device anonymity while providing sufficient information for security verification
3Reliability
If centralized attestation authority is used to verify devices, then security control is improved, but single point of failure risk increases
Solution Approach 1:
The centralized attestation authority is segmented into multiple distributed reporting nodes. Each node performs independent verification of device security attributes and contributes to the overall attestation process. This distribution eliminates the single point of failure inherent in centralized systems while maintaining coordinated security control
Solution Approach 2:
The patent combines the verification capabilities of multiple independent reporting nodes to achieve collective security control. The external verifier receives and processes attestation reports from multiple nodes, merging their verification results to form a comprehensive security assessment, thereby distributing control while maintaining effectiveness
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively protects IoT device privacy and security by ensuring that individual nodes are not compromised, and only high-risk data is reported, while maintaining network trust and integrity, thus addressing the challenge of balancing security and privacy in IoT networks.
Implementation Method 1
The processor may further decrypt the plurality of attestation reports. The processor may further process the decrypted plurality of attestation reports to obtain aggregated telemetry data
Implementation Method 2
an example of this is the Diffie-Hellman used in the case of two individuals A and B which agree on a secret encryption key and an encryption algorithm
Implementation Method 3
The systems and techniques described cluster network addresses together by detecting shared identifiers being used by the network addresses
Data Source
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AI summary
In one embodiment, a method includes receiving, in a system of an external verifier of a first network, a plurality of attestation reports and a plurality of attestation values from a plurality of reporting nodes of the first network, each of the plurality of attestation values randomly generated in the corresponding reporting node based on a common random seed value; determining whether at least a threshold number of the plurality of attestation values match; responsive to at least the threshold number of the plurality of attestation values matching, decrypting the plurality of attestation reports, processing the decrypted plurality of attestation reports to obtain aggregated telemetry data of the plurality of nodes, where identity of the plurality of nodes remains anonymous to the external verifier; and enforcing a security policy based at least in part on the aggregated telemetry data. Other embodiments are described and claimed.