Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection
This addresses privacy preservation for data owners in big data applications, offering improved flexibility in the utility-privacy trade-off, though it appears incremental as it builds on compressive-privacy methods.
The paper tackles the problem of balancing data utility and privacy in subspace projection by proposing RUCA, a method that maximizes classification performance while minimizing the ability to infer private information, showing significant outperformance over existing techniques on Census and Human Activity Recognition datasets.
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.