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There are numerous opportunities for adversaries to observe user behavior remotely on the web. Additionally, keystroke biometric algorithms have advanced to the point where user identification and soft biometric trait recognition rates are commercially viable. This presents a privacy concern because masking spatial information, such as IP address, is not sufficient as users become more identifiable by their behavior. In this work, the well-known Chaum mix is generalized to a scenario in which users are separated by both space and time with the goal of preventing an observing adversary from identifying or impersonating the user. The criteria of a behavior obfuscation strategy are defined and two strategies are introduced for obfuscating typing behavior. Experimental results are obtained using publicly available keystroke data for three different types of input, including short fixed-text, long fixed-text, and long free-text. Identification accuracy is reduced by 20% with a 25 ms random keystroke delay not noticeable to the user.
The Echo protocol tries to do secure location verification using physical limits imposed by the speeds of light and sound. While the protocol is able to guarantee that a certain object is within a certain region, it cannot ensure the authenticity of
In this paper we provide evidence of an emerging criminal infrastructure enabling impersonation attacks at scale. Impersonation-as-a-Service (ImpaaS) allows attackers to systematically collect and enforce user profiles (consisting of user credentials
In the cloud computing era, data privacy is a critical concern. Memory accesses patterns can leak private information. This data leak is particularly challenging for deep learning recommendation models, where data associated with a user is used to tr
With the rapid advancement of technology, different biometric user authentication, and identification systems are emerging. Traditional biometric systems like face, fingerprint, and iris recognition, keystroke dynamics, etc. are prone to cyber-attack
Deep learning has been widely applied in many computer vision applications, with remarkable success. However, running deep learning models on mobile devices is generally challenging due to the limitation of computing resources. A popular alternative