Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2020-04-21 , DOI: 10.1016/j.knosys.2020.105932 Ofir Landau , Aviad Cohen , Shirley Gordon , Nir Nissim
With the digitization of almost every aspect of our lives, privacy leakage in cyber space has become a pressing concern. Brain Computer Interface (BCI) systems have become more popular in recent years and are now being used for a variety of applications. BCI data represents an individual’s brain activity at a given time. Like many other kinds of data, BCI data can be utilized for malicious purposes. Electroencephalography (EEG) is one of the most popular brain activity acquisition methods of BCI applications. More specifically, BCI games, represent one of the main EEG applications. However, a malicious BCI application (e.g. game) could allow an attacker to take advantage of an unsuspecting user happily enjoying a game and record the user’s brain activity; by analyzing this data, the attacker can infer private information and characteristics regarding the user, without his/her consent or awareness. This study is the first to demonstrate the ability to predict and infer meaningful personality traits and cognitive abilities by analyzing resting-state EEG (rsEEG) recordings of an individual’s brain activity using a variety of machine learning methods. A comprehensive set of raw rsEEG scans, along with the dissociation level and executive function (EF) performance measures, for the 162 subjects were used in our evaluation. The best results we achieved were an accuracy of 73% for dissociation classification and less than 16% mean absolute error in predicting performance for all examined EFs. These encouraging results are better than those presented in prior research, both in terms of accuracy and data-validity and dataset size.
中文翻译:
注意您的隐私:使用机器学习方法通过BCI应用程序泄露隐私
随着我们生活中几乎每一个方面的数字化,网络空间的隐私泄露已成为迫切关注的问题。近年来,脑计算机接口(BCI)系统变得越来越流行,并且现在已用于各种应用程序中。BCI数据表示给定时间的个人大脑活动。像许多其他类型的数据一样,BCI数据可用于恶意目的。脑电图(EEG)是BCI应用程序中最流行的大脑活动获取方法之一。更具体地说,BCI游戏代表了主要的EEG应用程序之一。但是,恶意的BCI应用程序(例如游戏)可能允许攻击者利用毫无戒心的用户高兴地享受游戏并记录用户的大脑活动。通过分析这些数据,攻击者无需他/她的同意或察觉就可以推断有关该用户的私人信息和特征。这项研究是首次通过使用多种机器学习方法分析个人大脑活动的静止状态EEG(rsEEG)记录来证明预测和推断有意义的人格特质和认知能力的能力。我们对162名受试者使用了一套完整的原始rsEEG扫描以及分离水平和执行功能(EF)绩效指标。我们获得的最佳结果是,解离分类的准确度为73%,在预测所有已检查EF的性能时,平均绝对误差低于16%。在准确性,数据有效性和数据集大小方面,这些令人鼓舞的结果都优于以前的研究。