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[HongKe Solutions] How to solve the problem of cross-border data compliance in the era of data-driven change in automated driving R&D?

It is a time of great change driven by data. From ADAS to AD, every technological leap involves the collection, annotation and analysis of massive road data. Taking image data as an example, they are valuable resources for training and optimizing perception algorithms and enhancing system safety.

However, when the plan was to send this data to the overseas R&D team, the challenge of "data compliance" came to the fore.

Data Compliance Challenges in Globalized R&D

For any automotive company or technology provider with global expansion plans, cross-country R&D and testing is often unavoidable, such as verifying the robustness of algorithms in different traffic environments and climates. However, when real-world image data is used, it often captures a large amount of data. Personally Identifiable Information (PII)The following are some examples of this: facial features of pedestrians, clearly recognizable license plate numbers, and so on.

This is the heart of the challenge. When cross-border transfer of data becomes a new R&D requirement, companies are stepping into a "high-risk zone" of global privacy regulations.

Differences and Realities in Global Data Regulations

Since the European Union's General Data Protection Regulation (GDPR) set a milestone in global data protection, privacy legislation reforms have begun globally, with China's Personal Information Protection Law (PIPL) being one of the key components. Although these laws and regulations are all aimed at protecting personal information, they are not as comprehensive as the PIPL. Definitions, principles and cross-border rules The differences between the two are a huge challenge for global automakers.

Take GDPR and PIPL as an example:

  • Data Definition Scope

    • Article 4 of the GDPR: The definition is extremely broad and includes "any information relating to an identified or identifiable natural person", such as a face in image data or a car license plate number.

    • PIPL Article 4: Similarly broad, but explicitly excludes "anonymized information" to provide direction on data compliance.

  • Handling Principle Requirements

    • Article 5 of the GDPR: establishes six core principles, the first of which is "lawfulness, fairness and transparency".

    • PIPL Articles 5 and 6: emphasize the principles of "lawfulness, propriety, necessity, good faith" and "minimization of impact".

  • Cross-border Data Transfer Mechanism

    • GDPR Chapter 5: Centering on 'Adequacy Recognition' and Standard Contractual Clauses (SCCs).

    • PIPL Article 38: Adoption of the "security assessment, certification, and standard contract" option; Article 40 requires large-scale data processors to undergo a national security assessment.

This "one country, one policy" pattern forces automakers to refine their global data flows. Failure to do so may not only affect business expansion, but also face the possibility of Large fines and reputational riskIn the age of data sovereignty. In the age of data sovereignty, embracing compliance and utilizing cutting-edge technologies such as anonymization is a must for business survival and development.

Technology Breakthroughs: How to Balance Privacy and Data Value?

If the personal identifying information in the data can be removed completely and irreversibly through technical means, then the data can be legally circulated freely across borders for AI training and algorithmic analysis.
But here is the problem:How to protect privacy while retaining the value of data for R&D?

To this end, theHi-Tech Brighter AI Proposed an industry-leading AI-driven anonymization solution.

  1. Precision Blur

    • Automatically recognizes faces and license plates in images/videos.

    • Only the core area is treated, preserving the integrity of the background.

    • Provide high quality data base for AI training and machine learning.

  2. Full Body Blur

    • To further recognize the whole body contour of the pedestrian.

    • Prevents identification by indirect information such as posture, clothing, and tattoos.

    • Suitable for sensitive scenes: public surveillance, campus testing, etc.

  3. Deep Natural Anonymization Technique (DNAT)

    • Leveraging generative AI to replace faces and license plates with new, natural images instead of traditional mosaic masks.

    • Irreversible, truly anonymous: Generate unique and randomized images to ensure compliance with global regulations.

    • Retention of Properties: Gender, race, emoticons, and accessories (glasses, etc.) remain.

    • AI Training Friendly: In tasks such as target detection and semantic segmentation, the performance is almost the same as the original data.

Conclusion

Stricter and more harmonized data laws and regulations are the order of the day around the world. For enterprises competing for autonomous driving, it will be Data Compliance Shifting from a "cost center" to a "strategic advantage" will be the key to victory.

With flexible deployment modes (cloud, local, edge computing) and generative AI anonymization, Brighter AI provides a path for global automotive companies and suppliers to A clear, compliant and forward-looking path to data complianceThe

Go to the Brighter AI Suite product page

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