Trusted Processing Enclaves for Privacy-Preserving IoT Data Analysis
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Solution Overview
Problem
Current IoT and industrial IoT data management solutions lack an architecture that preserves user privacy when using individual-level user data in real time, failing to allow end users to request information without exposing identifying data, and existing solutions are not efficient or scalable for fast, responsive, and privacy-preserving data processing.
Innovation Solution
An architecture that enables data processing at the edge or in the cloud, allowing end users to define geospatial areas, request data from devices within those areas, process data anonymously, and delete the original data used for analysis, ensuring privacy by generating anonymous identifiers and returning only group-level analytical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If individual-level data is collected and processed in real time, then productivity and decision-making quality are improved, but user privacy is compromised due to exposure of identifying data
Solution Approach 1:
The patent introduces trusted processing environments (TPEs) and secure enclaves as intermediary layers between data collection and analysis. These intermediaries process individual-level data in isolated, secure environments that prevent unauthorized access to identifying information while still enabling real-time analytical processing. The TPE acts as a mediator that allows productivity improvement without direct exposure of sensitive data to end users or general processing systems.
Solution Approach 2:
The patent segments the data processing architecture into distinct isolated environments: untrusted environments for data collection, trusted processing environments for secure analysis, and controlled output interfaces for results delivery. This segmentation allows real-time processing of individual-level data to occur in isolated TPEs that are separated from the main system, preventing privacy compromise while maintaining processing capability.
2Loss of time
If data is processed quickly with minimal delay, then productivity is improved, but privacy protection mechanisms become more complex
Solution Approach 1:
The patent implements preliminary setup of trusted processing environments with pre-configured security policies, cryptographic keys, and access controls before data processing begins. This preliminary action allows the complex privacy protection architecture to be established once, enabling subsequent real-time data processing without repeated setup delays. The TPEs are pre-authenticated and pre-configured to handle individual-level data securely from the outset.
Solution Approach 2:
The trusted processing environments operate autonomously with built-in security mechanisms that automatically enforce privacy protections without requiring external intervention for each data processing operation. The TPEs self-manage cryptographic operations, access control enforcement, and secure data handling, reducing the operational complexity burden on external systems while maintaining rapid processing capabilities.
3Ease of operation
If end users can request and access individual-level data, then ease of operation is improved, but reliability of privacy protection deteriorates
Solution Approach 1:
The patent inverts the traditional data access model by allowing end users to request analytical results without directly accessing individual-level data. Instead of users querying and receiving raw data, the system processes requests through TPEs that automatically perform secure analysis and return only aggregated or anonymized results. This inversion maintains ease of operation for data requests while fundamentally protecting privacy by design, making privacy preservation reliable rather than optional.
Solution Approach 2:
The patent implements feedback mechanisms where the trusted processing environments provide confirmation to users about the nature and scope of data processed, without revealing sensitive individual-level information. The system feeds back analytical results and processing metadata that verify privacy protections were applied, maintaining user trust and ease of operation while ensuring reliable privacy preservation through transparent but protected processing verification.
Data Source
AI summary
The present disclosure relates to techniques for collection and processing of data over a network, and in particular to in providing generalized analysis for preserving privacy of data sources. In some embodiments, a system receives a request for analytical data made by a requester, wherein the request includes: information for identifying one or more data source devices to be queried for input data, a type of data processing to be performed on the input data for generating the analytical data, and a data type for the analytical data. In response to receiving the request, the system causes the one or more data source devices to be queried for the input data. The system causes a response that includes the analytical data to be sent to the requester. Other embodiments are described throughout the present disclosure.