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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 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]