Privacy-protective AI pertains to the creation of AI systems that safeguard confidential data during the processes of training and inference. A variety of methods, such as differential privacy, federated learning, homomorphic encryption, and secure multiparty computation are employed to accomplish this objective. The goal is to keep training data, user inputs, and model results confidential and secure. For example, differential privacy introduces statistical noise into the training data to inhibit the retrieval of individual-specific information. Federated learning facilitates model training across dispersed data sources without necessitating the sharing of raw data. Homomorphic encryption and secure multiparty computation allow for computations on encrypted data without revealing the raw details. These strategies are especially crucial in areas like biomedicine and medical imaging where privacy is a critical concern. The field of privacy-protective AI is a vibrant research area with continuous endeavors to enhance these techniques’ efficiency and scalability.
AI techniques that preserve privacy are essential for safeguarding sensitive data while still allowing its use in various applications. Here are some key examples where they can be applied:
1. Training of AI/ML Models
- Synthetic data that preserves privacy can be employed to train machine learning models, offering a scalable solution that protects confidential information.
2. Development of Products
- Synthetic data allows engineers and data scientists to access and use data without violating privacy, thereby fostering innovation and value generation in product development.
3. Monetization of Data
- Synthetic data that preserves privacy provides a compliant method to generate income from sensitive data streams without infringing on individual privacy.
4. Sharing of Data
- Businesses can leverage synthetic data that preserves privacy to share information with third parties, enhancing flexibility and addressing challenges related to sharing sensitive data.
5. Publication of Data
- Synthetic data that preserves privacy ensures no personal details are revealed when disseminating data with external stakeholders, thus protecting individual privacy.
Techniques such as federated learning and homomorphic encryption play an integral role in securing AI systems’ security and privacy while handling personal information. Furthermore, strategies like cryptographic hashing and differential privacy are used to achieve OCR preserving privacy in image-to-text applications.
