INFORMATION LAUNDERING FOR MODEL PRIVACY

Xinran Wang, Yu Xiang, Jun Gao, Jie Ding

Research output: Contribution to conferencePaperpeer-review

7 Scopus citations

Abstract

In this work, we propose information laundering, a novel framework for enhancing model privacy. Unlike data privacy that concerns the protection of raw data information, model privacy aims to protect an already-learned model that is to be deployed for public use. The private model can be obtained from general learning methods, and its deployment means that it will return a deterministic or random response for a given input query. An information-laundered model consists of probabilistic components that deliberately maneuver the intended input and output for queries of the model, so the model's adversarial acquisition is less likely. Under the proposed framework, we develop an information-theoretic principle to quantify the fundamental tradeoffs between model utility and privacy leakage, and derive the optimal design.

Original languageEnglish (US)
StatePublished - 2021
Event9th International Conference on Learning Representations, ICLR 2021 - Virtual, Online
Duration: May 3 2021May 7 2021

Conference

Conference9th International Conference on Learning Representations, ICLR 2021
CityVirtual, Online
Period5/3/215/7/21

Bibliographical note

Funding Information:
The last author was supported by the Army Research Office (ARO) under grant number W911NF-20-1-0222.

Publisher Copyright:
© 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

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