With the rapid development of the Internet of Things (IoT), more IoT devices are connected in the same network and communicate frequently for sharing data and switching instructions. So that the traditional centralized security architecture of IoT will be limited in terms of data storage space, data reliability, scalability, and operating costs. In this article, we propose a novel communication data management model based on blockchain technology. Specifically, all encrypted IoT communication data files are uploaded to the public cloud server for obtaining enough storage space, but the key information extracted from these data files (called “communication logs”) will be recorded in an “IoT ledger” maintained by all IoT devices. In fact, the IoT ledger is a kind of blockchain structured distributed database, which can solve the problem of data reliability, scalability and would not involve high operating costs. Besides, in order to efficiently search communication logs and not reveal any sensitive information of communication data, we design a secure search scheme over such blockchain database, the asymmetric scalar-product-preserving encryption (ASPE) approach is exploited to guarantee the data security, and the two layers index improves the search efficiency. Security analysis and experiments on synthetic data set show that our scheme is secure and efficient.

Recent works have demonstrated that text classifiers are vulnerable to universal adversarial triggers (UATs), which are concatenated to any original text from the dataset to mislead text classifiers. Existing methods for generating UATs are limited to a white-box setting, where the adversary needs access to the gradient information about the target model. In the more practical black-box setting, the adversary can only access the logit output of the target model, which increases the difficulty of crafting UATs. In this paper, we propose a framework for generating natural UATs using generative adversarial networks (NUAT-GAN) in the black-box setting. To update parameters of the generator in the black-box setting, we design a training generator algorithm with policy gradient (TGPG), in which the gradient of the target model is replaced with the policy gradient of reinforcement learning. On three text classification datasets, we evaluate the attack and the natural performance of UATs generated on LSTM, CNN and BERT models. Results show that UATs generated by NUAT-GAN can mislead the above models. The average values of the Attack Success Rate (ASR) and GPT-2 Loss of UATs are 0.81 and 9.10, respectively. The UATs can effectively attack online models, such as AllenNLP and ChatGPT. Moreover, we replace the reward given by the discriminator with GPT-2 Loss , the attack and the natural performance of UATs are close to those of NUAT-GAN. This shows that NUAT-GAN is extensible and can combined with the language model.

Multiple servers collaboration technology furnishes an effective storage management platform to the service providers. The massive data, which belong to disparate providers, possesses distinct access policies for each user. Authentication applied to multiple servers architecture is recognized as a remarkable mechanism for access control and authorization of users. In 2017, Jangirala et al. explored problems existing in the current research of authentication for multiple servers environment and presented a new solution. Nevertheless, we find that it is defenseless in impersonation attack, server spoofing attack, and fails to maintain users’ anonymity. In this paper, we detail the aforementioned faults and propose a remedy with a tripartite certification strategy. Correspondingly, the validation of BAN-logic focused on tripartite authentication protocol is put forward to demonstrate the security reliability. Comparative evaluation of other related solutions for the multiple servers architecture observes that our proposal has advantages over security attributes meanwhile provides a reliable guarantee of efficiency.