Designing a GDPR Compliance Microservice for IoT Backend Systems

Introduction

The General Data Protection Regulation (GDPR) is a comprehensive European privacy law that imposes strict rules on collecting, storing, and processing personal data. For IoT (Internet of Things) backend systems, ensuring compliance with the GDPR is particularly challenging. The vast volume of data generated by IoT devices introduces unique obstacles, especially in data access, deletion, and portability. Unlike traditional databases, IoT systems often store data across multiple sources — ranging from structured to unstructured data and time-series repositories. This fragmentation, coupled with the frequent and continuous nature of data collection, makes achieving GDPR compliance a complex and resource-intensive task.

In this blog post, we explore the critical challenges of designing a GDPR-compliant IoT backend and discuss how a well-architected microservice-based solution can address these concerns efficiently while maintaining scalability and fault tolerance.

Key Challenges

Designing a GDPR-compliant IoT backend involves overcoming several hurdles due to the nature of IoT data and the regulatory requirements imposed by GDPR. Below are some of the most pressing challenges:

• Heterogeneous Data Storage: IoT systems store data in various formats across multiple databases. Unstructured data may be stored in S3, time-series data in AWS Timestream, and structured data in AWS Aurora. Retrieving and aggregating user data from these sources requires a well-orchestrated approach.
• Massive Data Volume: With thousands of devices collecting data every few minutes, the volume of stored information grows exponentially. Handling GDPR requests like data access and deletion becomes computationally expensive and must be managed efficiently.
• Secure Request Handling: GDPR mandates that users must be able to access their personal data. However, querying vast amounts of data can place a significant burden on system resources, making it susceptible to abuse.
• Data-Object Mapping Complexity: Deleting a user’s data is not always straightforward. Some entities, such as IoT sensors, may be shared among multiple users. If a single user requests data deletion, should the sensor entity be removed entirely? The system must intelligently nullify foreign keys linking users to persistent objects instead of outright deletion.
• IoT Ecosystem Complexity: IoT systems integrate multiple devices and services, making it challenging to track which services hold specific user data.
• Scalability & Fault Tolerance: As the number of GDPR requests grows, the system must scale dynamically to ensure timely responses without affecting performance.
• Human-Readable Data Formatting: IoT data is typically stored in raw technical formats, whereas GDPR requires that users receive it in a comprehensible manner. This necessitates transformation mechanisms to deliver user-friendly data.

Successfully addressing these challenges requires a robust and scalable architecture that can efficiently manage compliance requests while optimizing system resources.

Solution Architecture

To tackle these challenges, a microservice-based architecture that efficiently handles GDPR requests while ensuring scalability, fault tolerance, and cost optimization is needed. Below is an overview of the approach:

[An illustration of the Physical Architecture of our Solution]

1. System Division: Proxy Layer and Worker Layer

The system is divided into two main components: the proxy layer and the worker layer. These components communicate asynchronously via AWS SQS queues to ensure seamless data processing.

• Proxy Layer: This acts as the central request handler, responsible for authenticating requests, enforcing rate limits (one request per day per user), and routing requests to the appropriate workers. It also ensures scalability by dynamically triggering the worker layer based on queue load.
• Worker Layer: This layer is responsible for interfacing with various data stores to fetch, delete, or process GDPR-related requests. Workers query structured and unstructured databases, retrieve or delete personal data, and queue results back to the proxy for further processing.

2. Daily Preprocessing for Efficient Request Handling

A scheduled AWS EventBridge event triggers a daily GET request to all integrated services, retrieving user IDs to map where customer data is stored. This preprocessing step ensures that when a user submits a GDPR request, the system knows exactly which services need to be queried, eliminating unnecessary processing and improving response time.

3. Request Processing and Fault Tolerance

Once a user submits a GDPR request:

• The proxy service authenticates the request and checks if the user has exceeded their daily request limit.
• If the request is valid, the proxy routes it only to the relevant workers that store data for that specific user.
• Each worker retrieves data, processes deletions if applicable, and sends responses back to the proxy via an SQS response queue.
• The proxy aggregates responses, transforms raw data into a user-friendly JSON format, and sends it back to the user.

To ensure fault tolerance:

• The proxy maintains a request log in an Aurora database, tracking which workers have responded.
• Unanswered requests are requeued automatically for retry processing.
• If a request repeatedly fails, the system triggers an admin alert for manual intervention.

4. Data Deletion with Entity Preservation

When handling deletion requests:

• Workers remove all personally identifiable information (PII) associated with the user.
• Foreign keys linking users to non-deletable entities (e.g., sensors) are nullified instead of deleting the entire object.
• Processed deletions are logged to maintain auditability.

5. Cost-Effective Processing with Nightly Execution

To optimize cost and ensure system stability, workers execute requests only at night via a cron job. This approach allows the system to handle massive data processing workloads during off-peak hours, ensuring GDPR compliance without degrading real-time system performance.

Conclusion

As IoT devices continue to proliferate, ensuring GDPR compliance becomes a growing challenge. The complexity of IoT data, the sheer volume of records, and the need for secure, scalable, and fault-tolerant systems make compliance a demanding task.

By designing a microservice-based architecture that leverages asynchronous communication, intelligent request routing, automated fault tolerance, and cost-efficient processing, we ensure that GDPR requests are handled efficiently while maintaining scalability.

At Concept Reply, we specialize in helping organizations design and implement IoT solutions that comply with the different EU regulations. Our expertise ensures that businesses remain compliant without sacrificing efficiency or performance. If you’re looking for a secure, scalable, and regulation-compliant IoT solution, get in touch with us today to explore how we can support your business.