research-article

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Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy

Authors:

Fatemehsadat Mireshghallah,

Mohammadkazem Taram,

Ahmed Taha Taha Elthakeb,

Hadi EsmaeilzadehAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 669 - 680

https://doi.org/10.1145/3442381.3449965

Published: 03 June 2021 Publication History

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Abstract

When receiving machine learning services from the cloud, the provider does not need to receive all features; in fact, only a subset of the features are necessary for the target prediction task. Discerning this subset is the key problem of this work. We formulate this problem as a gradient-based perturbation maximization method that discovers this subset in the input feature space with respect to the functionality of the prediction model used by the provider. After identifying the subset, our framework, Cloak, suppresses the rest of the features using utility-preserving constant values that are discovered through a separate gradient-based optimization process. We show that Cloak does not necessarily require collaboration from the service provider beyond its normal service, and can be applied in scenarios where we only have black-box access to the service provider’s model. We theoretically guarantee that Cloak’s optimizations reduce the upper bound of the Mutual Information (MI) between the data and the sifted representations that are sent out. Experimental results show that Cloak reduces the mutual information between the input and the sifted representations by 85.01% with only negligible reduction in utility (1.42%). In addition, we show that Cloak greatly diminishes adversaries’ ability to learn and infer non-conducive features.

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Cited By

Yang ZZhang YWang THua ZXia ZWeng JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Once-for-all: Efficient Visual Face Privacy Protection via Person-specific VeilsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681371(7705-7713)Online publication date: 28-Oct-2024
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WWW '21: Proceedings of the Web Conference 2021

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DOI:10.1145/3442381

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Cited By

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https://dl.acm.org/doi/10.1145/3664647.3681371
Liu JZhou JTian JSun W(2024)Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial ExamplesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365367620:7(1-27)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3653676
Ding SZhang LPan MYuan X(2024)PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00465(4704-4713)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00465
Prodomo VGonzalez RGramaglia M(2024)SCIPER: Secure Collaborative Inference via Privacy-Enhancing RegularizationIEEE Transactions on Privacy10.1109/TP.2024.3513254(1-12)Online publication date: 2024
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Yuan LChen WPu XZhang YLi HZhang YGao XEbrahimi T(2024)PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature CompensationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.338897619(4930-4944)Online publication date: 2024
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