Advancing AI while protecting user privacy

Choosing the right tools

The range of privacy-enhancing technologies is very broad, but there are some that are particularly relevant when it comes to AI and machine learning operations. These are typically more complex and address different needs on a case-by-case basis, so they offer limitations as well as opportunities.

• Federated Learning (FL) or Collaborative Learning (CL) It focuses on multiple entities working on distributed data stored on different devices but without data exchange. This method encourages collaboration but has the drawback of relying on third-party servers that may be subject to leaks.
• Secure Multiparty Computing (MPC) allows multiple parties to perform operations on inputs that are kept private in the original environment. On the downside, MPC can be particularly slow, as it requires a large number of cryptographic operations.
• Differential Privacy (DP) is a method of adding noise to data to protect individual identities while still providing accurate information, but adding noise to data requires a delicate balance and can also limit what you can do.
• Data Anonymization (DA) is another solution that removes personally identifiable information, allowing private data manipulation without compromising analytical capabilities. Despite being very popular, this technique poses a significant risk to privacy due to the increased risk of exposure due to third-party servers.
• Fully homomorphic encryption (FHE) is an encryption technique that allows blindly processing data without decrypting it, protecting it from external interference. The main drawback of FHE at the moment is its computation speed, which makes it slow to operate and can cause problems in various applications.

Fully Homomorphic Encryption (FHE): Mathematical Principles and Computational Requirements

Let's take a closer look at fully homomorphic encryption. FHE is a breakthrough encryption technique that allows computations on encrypted data without decrypting it. This capability is underpinned by the homomorphic property, where both addition and multiplication operations can be performed on the ciphertext, and the decrypted result will match the result of the operation performed on the plaintext. The encryption schemes commonly employed in FHE are based on lattice-based encryption and rely on the difficulty of mathematical problems such as learning with errors (LWE) and ring learning with errors (RLWE). In these schemes, the plaintext message is encrypted into a ciphertext, and arithmetic operations on the ciphertext correspond to operations on the plaintext.

The essence of FHE is to manage the noise inherent in the ciphertext, which grows with each operation. Effective noise management is essential to keep the noise within bounds for successful decryption. Techniques such as bootstrapping can be used to update the ciphertext to reduce the noise and make it easier to compute. However, this process is computationally intensive and still imposes a large overhead.

The computational requirements of FHE are substantial. The processing overhead for encryption, decryption, and homomorphic operations can be orders of magnitude slower than operations on plaintext. Although modern FHE methods optimize these operations, they still require significant computational resources. Furthermore, the large size of the ciphertext and the need for large keys and parameters to maintain security and control noise levels make FHE operations memory intensive. Bootstrapping, which is essential for noise management, increases computational and memory load, but advances have made it more efficient over time.

Use cases that combine AI with strong privacy protection

In real-world applications, organizations are successfully leveraging technologies such as FHE and differential privacy to integrate strong privacy protection with AI. One notable example is a large bank that is using AI-driven fraud detection with FHE. The bank aims to detect fraudulent transactions without compromising customer data. By encrypting customer transaction data with FHE, the bank can analyze the encrypted data using AI models trained to detect fraudulent patterns. As transactions are processed, they are encrypted and analyzed by AI models that perform calculations on the encrypted data. This approach allows the bank to detect fraud in real time while maintaining customer confidentiality and complying with strict data protection regulations. The bank reports that enhanced data privacy measures have significantly reduced fraud losses and increased customer trust.

In the healthcare sector, hospitals are using AI and differential privacy for predictive diagnosis. The objective is to utilize AI to predict patient outcomes and recommend treatments while ensuring the privacy of patient data. Patient data is anonymized using differential privacy techniques before being input into the AI ​​model. Differential privacy ensures that the output of the AI ​​model does not compromise individual patient data by adding controlled noise to the data. The predictive model provides diagnosis and treatment recommendations based on general trends without exposing individual patient information. This approach allows hospitals to leverage powerful AI tools to improve patient care and outcomes while maintaining patient confidentiality. Hospitals have observed improvements in patient care and operational efficiencies, and patient trust has increased due to data privacy efforts.

In conclusion, FHE and differential privacy are important advancements at the intersection of AI and data security, allowing organizations to harness the power of AI while preserving privacy. Despite massive increases in computing power, continued research and technological improvements are making these solutions more practical for real-world applications. Successful implementations in banking and healthcare demonstrate the potential these technologies have to revolutionize industries while protecting sensitive information.

see next: AI in Cybersecurity: What Organizations Need to Know

Informed Technology Decisions

Even if obstacles prevent us from overcoming these challenges and maximizing the potential benefits, the experience gained so far with previous policies such as the GDPR can help identify courses of action and precautions that regulators and those responsible for implementing the technology in private organizations should bear in mind.

• Incorporating continuous learning into the regulatory process: Regulators need to engage in continuing education and partnerships with technology companies to keep pace with technological advances. Regular training sessions and technical briefings can provide the insights needed to develop informed and effective policies.
• Adopt a privacy-by-design approach: Organizations need to incorporate privacy considerations into the design phase of their technology solutions. This proactive approach ensures that privacy becomes a fundamental part of technology development, not an afterthought.
• Encourage public-private partnershipsCollaboration between government and the private sector can help fill knowledge gaps and foster regulations that protect privacy while still allowing innovation to flourish. These partnerships also allow new technologies to be piloted in controlled environments to assess their impact before being fully deployed.

Making the most of great power in everyday life

Whenever major tech companies announce new technologies and developments, it's easy to get carried away with the spectacular announcements and wonders of innovative devices and features, but sometimes we struggle to connect it to our everyday lives. While the possibilities always seem endless, it's not always easy to clearly pinpoint how these new technologies will actually impact, and hopefully benefit, the services and functions that are essential to us all.

Artificial intelligence certainly fits into this story: ChatGPT was first introduced in early 2023 and has evolved from a tool to play around with into a resource that can be applied to a variety of situations and use cases.

For example, in the financial sector, the deployment of AI for fraud detection is a key area of ​​innovation. However, these systems often require access to sensitive personal data. FHE allows encrypted transactions to be analyzed in real time without exposing individual data points, thereby enhancing security while maintaining client confidentiality.

AI has the potential to transform healthcare, from personalized medicine to predictive diagnostics. However, patient data is highly sensitive, and implementing PETs such as differential privacy, which adds randomness to datasets to prevent personal identification, allows researchers to develop AI models without compromising patient privacy.

Another area where AI is increasingly exerting its influence is personalized marketing and e-commerce, where consumer data is heavily used to customize recommendations. To protect user privacy, companies can use synthetic data, a form of data anonymization that generates entirely new datasets that mimic the statistical properties of the original data. This allows AI systems to learn consumer preferences without accessing actual consumer data, thus protecting individual identities.

The interplay between advances in AI and maintaining privacy is delicate and complex, but by adopting a strategic and informed approach, we can harness the full potential of new technologies while upholding ethical obligations to privacy. The insights and proactive measures taken by developers and industry leaders highlight the importance of thoughtful innovation. As we navigate these challenges, it's critical that all stakeholders — developers, companies, and regulators — commit to continuous learning and adapting to ensure technological advances do not come at the expense of privacy and security.

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