Proposal Description

Session Overview:
In today's data-driven world, the importance of protecting individual privacy while extracting meaningful insights from data has become increasingly paramount. The session titled "Privacy in Statistics: Recent Advances" aims to explore the latest developments in privacy-preserving statistical methods and their applications across various domains. This session will provide a platform for researchers, statisticians, and practitioners to discuss recent advancements, challenges, and future directions in the field of privacy-enhancing statistical analysis.

Session Objectives:
1) To showcase recent advancements in privacy-preserving statistical methods.
2) To explore the applications of privacy-enhancing techniques in real-world scenarios.
3) To foster collaboration and knowledge exchange among researchers and practitioners working in the field of privacy in statistics.

Session Topics:
The session will cover a broad range of topics related to privacy in statistics, including but not limited to:
- Differential privacy: Techniques for adding noise to statistical queries to protect individual privacy while preserving data utility.
- Secure multiparty computation: Methods for performing computations on encrypted data without revealing sensitive information to any party involved.
- Privacy-preserving data mining: Algorithms for extracting patterns and insights from data without compromising individual privacy.

Conclusion:
The session on "Privacy in Statistics: Recent Advances" explores cutting-edge research, share best practices, and network with experts in the field. By fostering collaboration and knowledge exchange, this session aims to contribute to the advancement of privacy-preserving statistical methods and their broader adoption across different domains.