AI in Data Privacy

• Blockchain Council
• December 20, 2024

Artificial intelligence (AI) is reshaping how personal data is protected while ensuring progress in innovation through data use. By employing smarter methods, AI helps secure sensitive information throughout processing and analysis stages.

AI’s Contribution to Securing Personal Information

AI uses advanced methods to keep sensitive information safe while enabling its responsible use. These approaches include distributed learning models, privacy-preserving techniques like differential privacy, and methods that allow encrypted data to remain confidential even when processed.

Distributed Learning Without Sharing Data

A standout method in AI is distributed learning, where devices work together to improve machine learning models without transmitting raw information. Data stays on individual devices, and only updates are sent to a central system. This protects personal details while still improving algorithms.

Example: Google’s Gboard keyboard app utilizes this technique. It learns user typing patterns to improve its suggestions and autocorrect features without transferring sensitive information to external servers.

Protecting Identities Through Noise

Another method, differential privacy, works by blending random elements into datasets. This makes it almost impossible to identify individual details while still allowing accurate overall analysis. The process protects personal data even when sharing or evaluating large datasets.To know more about AI and its impact on data privacy, consider getting expert-led AI certifications by the Blockchain Council.

Example: The U.S. Census Bureau uses this approach to maintain respondent privacy in published statistics. By subtly modifying data with controlled randomness, they release valuable information without exposing individual identities.

Encrypting Data for Secure Processing

Homomorphic encryption is another breakthrough where data remains encrypted even while being processed. This ensures sensitive information is never exposed, and results are only accessible to authorized users after decryption.

Example: Microsoft has used this approach in cloud computing, enabling organizations to work with private information securely while keeping it encrypted during processing.

Innovations Leveraging AI for Privacy

Several companies have introduced AI-driven solutions to strengthen privacy and user security:

• Proton’s Tools for Enhanced Privacy
  Proton, a company renowned for encrypted email services, has introduced AI-powered features such as Sentinel for account protection and Scribe for assisting with email composition. These services are built with end-to-end encryption to ensure user data remains private while improving functionality.
• Apple’s Privacy-Focused AI Systems
  Apple has embedded AI features across its devices while addressing privacy concerns through solutions like Private Cloud Compute. This system processes data locally on devices or within secure environments, ensuring sensitive details aren’t accessible to unauthorized individuals.

Overcoming Barriers to Privacy Through AI

While AI’s capabilities in privacy are transformative, they aren’t without challenges:

• Preserving Data Utility: Balancing the need for useful analysis while safeguarding individual information is a fine line to walk. Techniques like adding noise must maintain data relevance.
• Adhering to Regulations: Companies must work within strict privacy laws, such as GDPR, which mandate how data is collected, stored, and used. This adds complexity to developing AI systems.
• Addressing Ethical Questions: It’s critical to ensure that AI respects privacy and doesn’t introduce bias, as maintaining user trust relies heavily on fairness and accountability.

The Future of Privacy with AI

The integration of techniques such as distributed learning, privacy-preserving measures, and encrypted processing marks a significant step forward in safeguarding personal information. Companies like Proton and Apple showcase how AI can advance user experiences without compromising privacy.

As AI continues to advance, the focus must remain on resolving challenges like balancing usability with privacy protection, ensuring compliance with regulatory frameworks, and addressing ethical concerns to build fair and trustworthy systems. This approach is essential for maintaining data privacy while benefiting from the growing possibilities of AI-driven technology.