Search Results for author: Sergey Yekhanin

Found 7 papers, 4 papers with code

Differentially Private Synthetic Data via Foundation Model APIs 2: Text

1 code implementation4 Mar 2024 Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin

Lin et al. (2024) recently introduced the Private Evolution (PE) algorithm to generate DP synthetic images with only API access to diffusion models.

Privacy Preserving

Differentially Private Synthetic Data via Foundation Model APIs 1: Images

1 code implementation24 May 2023 Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin

We further demonstrate the promise of applying PE on large foundation models such as Stable Diffusion to tackle challenging private datasets with a small number of high-resolution images.

Differentially Private Fine-tuning of Language Models

2 code implementations ICLR 2022 Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang

For example, on the MNLI dataset we achieve an accuracy of $87. 8\%$ using RoBERTa-Large and $83. 5\%$ using RoBERTa-Base with a privacy budget of $\epsilon = 6. 7$.

Text Generation

Differentially Private Set Union

1 code implementation ICML 2020 Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin

Known algorithms for this problem proceed by collecting a subset of items from each user, taking the union of such subsets, and disclosing the items whose noisy counts fall above a certain threshold.

Collecting Telemetry Data Privately

no code implementations NeurIPS 2017 Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin

In particular, existing LDP algorithms are not suitable for repeated collection of counter data such as daily app usage statistics.

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