Hot PATE: Private Aggregation of Distributions for Diverse Task
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914068587085824 |
|---|---|
| author | Cohen, Edith Cohen-Wang, Benjamin Lyu, Xin Nelson, Jelani Sarlos, Tamas Stemmer, Uri |
| author_facet | Cohen, Edith Cohen-Wang, Benjamin Lyu, Xin Nelson, Jelani Sarlos, Tamas Stemmer, Uri |
| contents | The Private Aggregation of Teacher Ensembles (PATE) framework enables privacy-preserving machine learning by aggregating responses from disjoint subsets of sensitive data. Adaptations of PATE to tasks with inherent output diversity such as text generation, where the desired output is a sample from a distribution, face a core tension: as diversity increases, samples from different teachers are less likely to agree, but lower agreement results in reduced utility for the same privacy requirements. Yet suppressing diversity to artificially increase agreement is undesirable, as it distorts the output of the underlying model, and thus reduces output quality.
We propose Hot PATE, a variant of PATE designed for diverse generative settings. We formalize the notion of a diversity-preserving ensemble sampler and introduce an efficient sampler that provably transfers diversity without incurring additional privacy cost. Hot PATE requires only API access to proprietary models and can be used as a drop-in replacement for existing Cold PATE samplers. Our empirical evaluations corroborate and quantify the benefits, showing significant improvements in the privacy utility trade-off on evaluated in-context learning tasks, both in preserving diversity and in returning relevant responses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02132 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hot PATE: Private Aggregation of Distributions for Diverse Task Cohen, Edith Cohen-Wang, Benjamin Lyu, Xin Nelson, Jelani Sarlos, Tamas Stemmer, Uri Machine Learning Artificial Intelligence Cryptography and Security Data Structures and Algorithms The Private Aggregation of Teacher Ensembles (PATE) framework enables privacy-preserving machine learning by aggregating responses from disjoint subsets of sensitive data. Adaptations of PATE to tasks with inherent output diversity such as text generation, where the desired output is a sample from a distribution, face a core tension: as diversity increases, samples from different teachers are less likely to agree, but lower agreement results in reduced utility for the same privacy requirements. Yet suppressing diversity to artificially increase agreement is undesirable, as it distorts the output of the underlying model, and thus reduces output quality. We propose Hot PATE, a variant of PATE designed for diverse generative settings. We formalize the notion of a diversity-preserving ensemble sampler and introduce an efficient sampler that provably transfers diversity without incurring additional privacy cost. Hot PATE requires only API access to proprietary models and can be used as a drop-in replacement for existing Cold PATE samplers. Our empirical evaluations corroborate and quantify the benefits, showing significant improvements in the privacy utility trade-off on evaluated in-context learning tasks, both in preserving diversity and in returning relevant responses. |
| title | Hot PATE: Private Aggregation of Distributions for Diverse Task |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Data Structures and Algorithms |
| url | https://arxiv.org/abs/2312.02132 |