Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909829178589184 |
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| author | Vepakomma, Praneeth Ponkshe, Kaustubh |
| author_facet | Vepakomma, Praneeth Ponkshe, Kaustubh |
| contents | Traditional collaborative learning approaches are based on sharing of model weights between clients and a server. However, there are advantages to resource efficiency through schemes based on sharing of embeddings (activations) created from the data. Several differentially private methods were developed for sharing of weights while such mechanisms do not exist so far for sharing of embeddings. We propose Ours to learn a privacy encoding network in conjunction with a small utility generation network such that the final embeddings generated from it are equipped with formal differential privacy guarantees. These privatized embeddings are then shared with a more powerful server, that learns a post-processing that results in a higher accuracy for machine learning tasks. We show that our co-design of collaborative and private learning results in requiring only one round of privatized communication and lesser compute on the client than traditional methods. The privatized embeddings that we share from the client are agnostic to the type of model (deep learning, random forests or XGBoost) used on the server in order to process these activations to complete a task. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_05581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption Vepakomma, Praneeth Ponkshe, Kaustubh Machine Learning Cryptography and Security Traditional collaborative learning approaches are based on sharing of model weights between clients and a server. However, there are advantages to resource efficiency through schemes based on sharing of embeddings (activations) created from the data. Several differentially private methods were developed for sharing of weights while such mechanisms do not exist so far for sharing of embeddings. We propose Ours to learn a privacy encoding network in conjunction with a small utility generation network such that the final embeddings generated from it are equipped with formal differential privacy guarantees. These privatized embeddings are then shared with a more powerful server, that learns a post-processing that results in a higher accuracy for machine learning tasks. We show that our co-design of collaborative and private learning results in requiring only one round of privatized communication and lesser compute on the client than traditional methods. The privatized embeddings that we share from the client are agnostic to the type of model (deep learning, random forests or XGBoost) used on the server in order to process these activations to complete a task. |
| title | Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2510.05581 |