Our latest research, papers, and insights on synthetic data, privacy-preserving AI, and responsible technology
We present a comprehensive survey of privacy-preserving synthetic data generation techniques, covering differential privacy, k-anonymity, and utility preservation methods.
This paper examines the trade-off between data utility and privacy preservation in healthcare data synthesis, with practical recommendations for implementation.
We propose a framework for measuring and maximizing the positive impact of AI systems on UN Sustainable Development Goals.
Exploring methods for detecting and mitigating bias in synthetic data generation to ensure fair and equitable AI systems.
Case studies from financial services demonstrating practical applications of differential privacy for regulatory compliance.
Guidelines and best practices for creating comprehensive model cards and data sheets to promote transparency in AI systems.
Our research focuses on three key areas that drive responsible AI innovation
Advancing techniques for generating high-utility synthetic datasets while preserving privacy and statistical fidelity.
Developing robust privacy-preserving techniques for data sharing and analysis.
Creating frameworks for measuring AI impact and ensuring responsible deployment.
We're always open to research collaborations, partnerships, and academic exchanges. Let's work together to advance responsible AI.
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