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Ensuring security in electronic examination systems represents a significant challenge, particularly when practical considerations dictate that most involved parties cannot be fully trusted due to self-interest. To enhance the security, we introduce auditability to e-exam systems, enabling an auditing authority to verify the system integrity. This auditability not only ensures system robustness but also creates an opportunity to grant communication between candidates and examiners, allowing for clarification on unclear questions during exams. Additionally, the implementation of attribute-based certifications ensures anonymity for both candidates and examiners throughout all stages of the exam, with the option for revocation in case of audit-detected fraud. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters since the model consists of several layers and each layer contains its own structure and parameters. The layers in a deep learning model of MixNN are fully decentralized. It hides communication address, layer parameters and operations, and forward as well as backward message flows among non-adjacent layers using the ideas from mix networks. MixNN has the following advantages: (i) an adversary cannot fully control all layers of a model, including the structure and parameters; (ii) even some layers may collude but they cannot tamper with other honest layers; (iii) model privacy is preserved in the training phase. We provide detailed descriptions for deployment. In one classification experiment, we compared a neural network deployed in a virtual machine with the same one using the MixNN design on the AWS EC2. The result shows that our MixNN retains less than 0.001 difference in terms of classification accuracy, while the whole running time of MixNN is about 7.5 times slower than the one running on a single virtual machine. [ABSTRACT FROM AUTHOR]

Cyberattacks on both databases and critical infrastructure have threatened public and private sectors. Ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this article, we survey 24 articles from 2008 to 2018 that use game theory to model defensive deception for cybersecurity and privacy. Then, we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their information structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models that can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy. [ABSTRACT FROM AUTHOR]

mix network is an anonymous communication channel usually employed in e-voting applications. A relation attack is a serious threat to privacy of any mix network and can attack various mix networks in many ways. At present, there is no efficient countermeasure to relation attacks in general. In this paper, a novel countermeasure against relation attacks is proposed. It can prevent any relation attack in mix-based e-voting schemes. It adopts a new encryption algorithm specially designed to be robust against relation attacks. The new countermeasure does not need any costly operation and is more efficient than the existing countermeasures. The new countermeasure is applied to voting and shown to work effectively. It is illustrated to protect the existing mix-based e-voting schemes from any relation attack. [ABSTRACT FROM AUTHOR]