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pro vyhledávání: '"Lut, Yuliia"'
Autor:
Negoescu, Diana M., Gonzalez, Humberto, Orjany, Saad Eddin Al, Yang, Jilei, Lut, Yuliia, Tandra, Rahul, Zhang, Xiaowen, Zheng, Xinyi, Douglas, Zach, Nolkha, Vidita, Ahammad, Parvez, Samorodnitsky, Gennady
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. The metric requires only black-box access to model predictions, does not req
Externí odkaz:
http://arxiv.org/abs/2307.11280
Autor:
Lut, Yuliia
Due to technological development, personal data has become more available to collect, store and analyze. Companies can collect detailed browsing behavior data, health-related data from smartphones and smartwatches, voice and movement recordings from
Accurately analyzing and modeling online browsing behavior play a key role in understanding users and technology interactions. In this work, we design and conduct a user study to collect browsing data from 31 participants continuously for 14 days and
Externí odkaz:
http://arxiv.org/abs/2108.06745
Publikováno v:
Proceedings of the International Conference on Machine Learning (2020) Pages 958-968
The change-point detection problem seeks to identify distributional changes in streams of data. Increasingly, tools for change-point detection are applied in settings where data may be highly sensitive and formal privacy guarantees are required, such
Externí odkaz:
http://arxiv.org/abs/1910.01327