// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Privacy Preserving
Technologies or methods designed to protect sensitive information while still allowing data to be used.
TECHNICAL DEFINITION
Encompassing a range of cryptographic and algorithmic techniques (e.g., homomorphic encryption, differential privacy, federated learning) that enable computation or analysis on data while minimizing or eliminating the exposure of sensitive underlying information.
BACKGROUND
Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Data privacy techniques
- privacy-enhancing technologies (PETs)
- secure data processing
USAGE NOTE
Privacy-preserving AI is a growing field focused on developing ethical and secure AI systems.
DEVELOPERS
Organizations developing technology related to Privacy Preserving.
An open-source community building technologies for privacy-preserving artificial intelligence, enabling secure and private federated learning and data analysis without centralizing sensitive data, which is critical for AI engineering and prompt design where data privacy is paramount.
Engages in extensive research and application of privacy-preserving machine learning techniques, including differential privacy and federated learning, across its AI products and research initiatives. This allows for the development and deployment of AI models, potentially including those used in prompt engineering, while safeguarding user data.
Develops and integrates privacy-enhancing technologies such as confidential computing, homomorphic encryption, and differential privacy into AI platforms and services. This enables secure AI engineering and the processing of sensitive prompts or data within AI models in a protected environment.
A leader in trusted AI, privacy-enhancing technologies, and confidential computing, developing frameworks and tools that allow AI systems to operate on sensitive data while maintaining privacy. Their work is vital for secure AI engineering and responsible prompt design in enterprise settings.
Provides a platform for secure computation, utilizing technologies like Secure Multi-Party Computation (MPC) and Homomorphic Encryption (HE) to enable data scientists and AI engineers to work with sensitive data and prompts without ever exposing the raw information, ensuring privacy in AI development and deployment.
Specializes in confidential AI, leveraging confidential computing to ensure that AI models, including large language models, can process sensitive data and prompts in a fully encrypted and isolated environment, protecting intellectual property and user privacy during inference and fine-tuning.
Offers a privacy-preserving AI platform that enables data scientists and AI engineers to safely work with sensitive data using synthetic data generation and differential privacy, crucial for developing and testing AI models and prompt strategies without exposing real personal information.
Focuses on providing secure infrastructure for deploying and operating AI models, particularly LLMs, using confidential computing. This ensures that prompts and the data generated by the models remain private and protected from unauthorized access during the AI engineering lifecycle.