Deep Learning(DL) techniques have gained significant importance in the recent past due to their vast applications. However, DL is still prone to several attacks, such as the Membership Inference Attack (MIA), based on the memorability of training data. MIA aims at determining the presence of specific data in the training dataset of the model with substitute model of similar structure to the objective model. As MIA relies on the substitute model, they can be mitigated if the substitute model is not clear about the network structure of the objective model. To solve the challenge of shadow-model construction, this work presents L-Leaks, a member inference attack based on Logits. L-Leaks allow an adversary to use the substitute model's information to predict the presence of membership if the shadow and objective model are similar enough. Here, the substitute model is built by learning the logits of the objective model, hence making it similar enough. This results in the substitute model having sufficient confidence in the member samples of the objective model. The evaluation of the attack's success shows that the proposed technique can execute the attack more accurately than existing techniques. It also shows that the proposed MIA is significantly robust under different network models and datasets.

With the development of wireless sensor networks, IoT devices are crucial for the Smart City; these devices change people’s lives such as e-payment and e-voting systems. However, in these two systems, the state-of-art authentication protocols based on traditional number theory cannot defeat a quantum computer attack. In order to protect user privacy and guarantee trustworthy of big data, we propose a new identity-based blind signature scheme based on number theorem research unit lattice, this scheme mainly uses a rejection sampling theorem instead of constructing a trapdoor. Meanwhile, this scheme does not depend on complex public key infrastructure and can resist quantum computer attack. Then we design an e-payment protocol using the proposed scheme. Furthermore, we prove our scheme is secure in the random oracle, and satisfies confidentiality, integrity, and non-repudiation. Finally, we demonstrate that the proposed scheme outperforms the other traditional existing identity-based blind signature schemes in signing speed and verification speed, outperforms the other lattice-based blind signature in signing speed, verification speed, and signing secret key size.

Voice over Long-Term Evolution enables reliable transmission among enormous Internet of Things devices, by providing end-to-end quality of service for Internet protocol–based services such as audio, video, and multimedia messaging. The research of covert timing channels aims at transmitting covert message stealthily to the receiver using variations of timing behavior. Existing approaches mainly modulate the covert message into inter-packet delays of overt traffic, which are not suitable for Voice over Long-Term Evolution, since most of the inter-packet delays of Voice over Long-Term Evolution traffic are of regular distribution, and any modification on inter-packet delays is easy to be detected. To address the issue, in this work, we propose a novel covert timing channel for the video stream in Voice over Long-Term Evolution, which modulates the covert message by deliberately dropping out video packets. Based on the two-dimensional mapping matrix, the blocks of covert message are mapped into dropout-packet sequence numbers. To recover the covert message, the receiver retrieves the sequence numbers of lost packets and identifies them to be translated into blocks of the covert message. To evaluate our scheme, the simulations with different packet loss rates are conducted to validate the undetectability, throughput, and robustness, finally, the results show that this scheme is effective and reliable.