In the era of big data and the Internet of Things (IoT), data owners need to share a large amount of data with the intended receivers in an insecure environment, posing a trade-off issue between user privacy and data utility. The privacy utility trade-off was facilitated through a privacy funnel based on mutual information. Nevertheless, it is challenging to characterize the mutual information accurately with small sample size or unknown distribution functions. In this article, we propose a privacy funnel based on mutual information neural estimator (MINE) to optimize the privacy utility trade-off by estimating mutual information. Instead of computing mutual information in traditional way, we estimate it using an MINE, which obtains the estimated mutual information in a trained way, ensuring that the estimation results are as precise as possible. We employ estimated mutual information as a measure of privacy and utility, and then form a problem to optimize data utility by training a neural network while the estimator's privacy discourse is less than a threshold. The simulation results also demonstrated that the estimated mutual information from MINE works very well to approximate the mutual information even with a limited number of samples to quantify privacy leakage and data utility retention, as well as optimize the privacy utility trade-off.
翻译:在海量数据和物联网(IoT)时代,数据所有者需要在一个不安全的环境中与预定接收者分享大量数据,这在用户隐私和数据实用性之间造成了权衡问题。隐私公用事业交换是通过基于相互信息的隐私漏斗促进的。然而,以小样本大小或未知分布功能来准确描述相互信息具有挑战性。在本篇文章中,我们提议基于相互信息神经测量仪(MINE)的隐私漏斗,以便通过估计相互信息优化隐私效用交换。我们估计它不是用传统方式计算相互信息,而是使用MIME,它以经过培训的方式获取估计的相互信息,确保估算结果尽可能准确。我们使用估计的相互信息作为衡量隐私和实用性的尺度,然后形成一个问题,通过培训神经网络来优化数据效用,而测量员的隐私谈话还不到一个门槛。模拟结果还表明,由MIE估计的相互信息非常有效,即使以有限的样本数量来量化隐私渗漏和数据使用性保密性保持,也是最优化的。