Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)

Ant Shared Intelligence will host a workshop at the top‑tier computer security conference ACM CCS 2020, which takes place in Orlando, USA from November 9‑13, 2020. The workshop targets researchers and practitioners in machine learning and security to discuss recent industrial advances and real‑world case studies in privacy‑preserving machine learning.

With growing privacy regulations and the increasing importance of data for AI, protecting user privacy while enabling multi‑party data collaboration has become a hot research topic. This workshop focuses on the practice of privacy‑preserving machine learning, covering, but not limited to, the following technologies and applications:

Secure multi‑party computation techniques (e.g., secret sharing and garbled circuits)

Homomorphic encryption techniques

Trusted execution environment (TEE) based approaches

Centralized and decentralized protocols for learning on encrypted data

Differential privacy

Privacy‑preserving machine learning methods (e.g., privacy‑preserving logistic regression, privacy‑preserving neural networks)

Collaborative learning / federated learning

(Privacy‑preserving) transfer learning: multi‑party secure fraud detection, privacy‑preserving recommendation, marketing, crowdsourcing, etc.

Submissions are currently being solicited; papers must be no longer than four pages, and the deadline for manuscript submission is June 21, 2020. Authors may submit papers that have been recently published or are under review. Details are available on the workshop website: https://sci-workshops.alipay.com/CCS2020 .

The program committee is chaired by Zhang Benyu, Chief Scientist of Ant Shared Intelligence Lab, Raluca Ada Popa (co‑founder of Berkeley RISELab), Stanford DAWN professor, Spark founder Matei Zaharia, Texas A&M professor Guofei Gu, and Zhejiang University professor Ji Shouliang. Committee members come from Alibaba, Ant Group, Google, IBM, and leading universities worldwide.

Ant Shared Intelligence, established in 2016, researches collaborative AI under privacy and data‑security constraints. Its four foundational directions are secure multi‑party computation, trusted execution environments, differential privacy, and federated learning, which together address the challenges of using multi‑party data for AI training and analysis.