Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection
arXiv:1702.07976
Abstract
With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life. Nevertheless, it has also opened new venues for breaching users' privacy. Hence it is highly required to develop techniques that enable data owners to privatize their data while keeping it useful for intended applications. Existing methods, however, do not offer enough flexibility for controlling the utility-privacy trade-off and may incur unfavorable results when privacy requirements are high. To tackle these drawbacks, we propose a compressive-privacy based method, namely RUCA (Ratio Utility and Cost Analysis), which can not only maximize performance for a privacy-insensitive classification task but also minimize the ability of any classifier to infer private information from the data. Experimental results on Census and Human Activity Recognition data sets demonstrate that RUCA significantly outperforms existing privacy preserving data projection techniques for a wide range of privacy pricings.
Submitted to ICASSP 2017
Cited by in corpus (6)
- Multilayer Nonlinear Processing for Information Privacy in Sensor Networks
- Utility-aware Privacy-preserving Data Releasing
- Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning
- Compressive Privacy for a Linear Dynamical System
- On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks
- Decentralized Detection with Robust Information Privacy Protection