// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Data Privacy
Protecting personal information from unauthorized access and use.
TECHNICAL DEFINITION
The ethical and legal imperative to safeguard personally identifiable information (PII) and sensitive data within AI systems, ensuring compliance with regulations like GDPR and CCPA.
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
- Information privacy
- personal data protection
- privacy protection
USAGE NOTE
Data privacy is crucial for building trust in AI applications that handle user data.
DEVELOPERS
Organizations developing technology related to Data Privacy.
An open-source community building technology for privacy-preserving artificial intelligence, enabling secure AI development and data collaboration using techniques like federated learning and differential privacy.
Through Google AI and Google Cloud, Google develops and implements privacy-enhancing technologies such as differential privacy and federated learning, integral for secure AI engineering and protecting user data in AI applications.
Microsoft Azure AI and Microsoft Research actively develop and integrate privacy-preserving AI techniques, responsible AI principles, and data governance tools into their AI platforms, supporting secure and ethical AI engineering.
IBM Research and IBM Watson offer solutions for AI governance, data privacy, and security in AI, including frameworks for managing privacy risks throughout the AI lifecycle from data preparation to model deployment.
Zama specializes in homomorphic encryption, a technology that allows computations on encrypted data, enabling privacy-preserving machine learning and AI model training without decrypting sensitive information.
Hazy provides a synthetic data generation platform that enables AI engineers and data scientists to create realistic, statistically representative synthetic data for AI model development, testing, and training while preserving privacy.
Sarus offers a platform for privacy-preserving data science and AI development, leveraging techniques like differential privacy and synthetic data to allow secure analysis and model building on sensitive datasets.
Privitar develops data privacy software that helps organizations manage and protect sensitive data, making it safe and compliant for use in AI model training, analytics, and development workflows.
Meta AI conducts extensive research and development in privacy-preserving AI, including federated learning and secure multi-party computation, to build AI applications that prioritize user data privacy.