Paper Info
Title
Privacy-preserving Machine Learning through Data Obfuscation.
Abstract
As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications. While it is prevalent to outsource model training and serving tasks in the cloud, it is important to protect the privacy of sensitive samples in the training dataset and prevent information leakage to untrusted third parties. Past work have shown that a malicious machine learning service provider or end user can easily extract critical information about the training samples, from the model parameters or even just model outputs. In this paper, we propose a novel and generic methodology to preserve the privacy of training data in machine learning applications. Specifically we introduce an obfuscate function and apply it to the training data before feeding them to the model training task. This function adds random noise to existing samples, or augments the dataset with new samples. By doing so sensitive information about the properties of individual samples, or statistical properties of a group of samples, is hidden. Meanwhile the model trained from the obfuscated dataset can still achieve high accuracy. With this approach, the customers can safely disclose the data or models to third-party providers or end users without the need to worry about data privacy. Our experiments show that this approach can effective defeat four existing types of machine learning privacy attacks at negligible accuracy cost.
Year
Venue
Field
2018
arXiv: Cryptography and Security
Information leakage,End user,Computer security,Computer science,Outsourcing,Service provider,Artificial intelligence,Obfuscation,Information privacy,Information sensitivity,Machine learning,Cloud computing
DocType
Volume
Citations 
Journal
abs/1807.01860
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Tianwei Zhang18621.44
Zecheng He2255.05
Ruby Lee32460261.28