Epsilon*: Privacy Metric for Machine Learning Models

no code implementations • 21 Jul 2023 • Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany, Jilei Yang, Yuliia Lut, Rahul Tandra, Xiaowen Zhang, Xinyi Zheng, Zach Douglas, Vidita Nolkha, Parvez Ahammad, Gennady Samorodnitsky

We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies.

Inference Attack Membership Inference Attack

Sales Channel Optimization via Simulations Based on Observational Data with Delayed Rewards: A Case Study at LinkedIn

no code implementations • 16 Sep 2022 • Diana M. Negoescu, Pasha Khosravi, Shadow Zhao, Nanyu Chen, Parvez Ahammad, Humberto Gonzalez

This opens questions regarding not only which decision-making policies would perform best in practice, but also regarding the impact of different data collection protocols on the performance of various policies trained on the data, or the robustness of policy performance with respect to changes in problem characteristics such as action- or reward- specific delays in observing outcomes.

Decision Making