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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]

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.

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]

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.

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]

The article offers information on Scantegrity, an electronic voting system. It is a commonly held view that electronic elections lead to improvements in terms of accuracy and speed of vote counts, but there is still much debate as to the efficacy of electronic voting. The system is said to be vulnerable to attack when an adversary changes the confirmation-code mappings, changes flags in an election's switchboard, and modifies the Scantegrity software.

Shuffling is an effective method to build a publicly verifiable mix network to implement verifiable anonymous channels that can be used for important cryptographic applications like electronic voting and electronic cash. One shuffling scheme by Groth is claimed to be secure and efficient. However, its soundness has not been formally proven. An attack against the soundness of this shuffling scheme is presented in this paper. Such an attack compromises the soundness of the mix network based on it. Two new shuffling protocols are designed on the basis of Groth's shuffling and batch verification techniques. The first new protocol is not completely sound, but is formally analyzed in regards to soundness, so it can be applied to build a mix network with formally proven soundness. The second new protocol is completely sound, so is more convenient to apply. Formal analysis in this paper guarantees that both new shuffling protocols can be employed to build mix networks with formally provable soundness. Both protocols prevent the attack against soundness in Groth's scheme. Both new shuffling protocols are very efficient as batch-verification-based efficiency-improving mechanisms have been adopted. The second protocol is even simpler and more elegant than the first one as it is based on a novel batch cryptographic technique [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]

The batch verification technique of Bellare et al. is extended to verification of several frequently employed zero-knowledge proofs. The new techniques are correct, sound, efficient, and can be widely applied. Specific applications are discussed in detail, including batch ZK proof and verification of validity of encryption (or reencryption) and batch ZK proof and verification of validity of decryption. Considerable efficiency improvements are gained in these two applications without compromising security. As a result, efficiency of the practical cryptographic systems (such as mix networks) based on these two applications is dramatically improved. [ABSTRACT FROM AUTHOR]