隐私保护子空间投影的比率效用与成本分析
Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection
AI总结：
提出基于压缩隐私的RUCA方法，通过比率效用与成本分析优化效用-隐私权衡，在隐私敏感分类任务中最大化性能并最小化隐私推断能力，实验表明优于现有技术。
摘要：
随着连接到互联网的设备数量迅速增加，大数据已应用于人类生活的各个领域。然而，它也开辟了侵犯用户隐私的新途径。因此，迫切需要开发技术，使数据所有者能够私有化其数据，同时保持其对预期应用的实用性。然而，现有方法在控制效用-隐私权衡方面不够灵活，并且在隐私要求高时可能产生不利结果。为了解决这些缺点，我们提出了一种基于压缩隐私的方法，即RUCA（比率效用与成本分析），它不仅可以最大化隐私不敏感分类任务的性能，还可以最小化任何分类器从数据中推断私人信息的能力。在人口普查和人类活动识别数据集上的实验结果表明，RUCA在广泛的隐私定价范围内显著优于现有的隐私保护数据投影技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
