An efficient signcryption scheme for vehicular satellite-based networks

With the widespread of the vehicular ad-hoc network (VANET), a huge number of vehicles are connecting to networks. To provide the position of these vehicles, the global position system (GPS) is required. Usually, the GPS is commoned with internal sensors mounted inside the vehicle. Thus, the communication with this sensor is needed when we need to specify the position of vehicle through the satellite. This communication is done by using a secure channels. However, the authentication and privacy are deemed as the main goal of the network communication. Therefore, an efficient signcryption scheme for vehicular satellite-based network (SVSN) is proposed in this paper. The proposed scheme meets the security demands for VANETs, for instance authentication, unforgeability, confidentiality, and integrity. Based on the Discrete Logarithm (DL) problem, the presented scheme is secure. Compared with the existing signcryption schemes, the performance analysis show that our proposed scheme is more suitable for vehicular satellite networks.

Quantum Algorithms

We have come a long way from Chap. 10.1007/978-3-030-61601-4_1 To recap on what we have learnt, we have understood important quantum mechanical phenomena such as superposition and measurement (through the Stern-Gerlach and Mach-Zehnder experiments). We have also learnt that while quantum computers can in principle break classical encryption protocols, they can also be used to make new secure channels of communication. Furthermore, we have applied quantum logic gates to qubits to perform quantum computations. With entanglement, we teleported the information in an unknown qubit to another qubit. This is quite a substantial achievement.

Quantum Secure Communication

Cryptography is a method of secure communication between two or more parties. The crucial step is exchanging a key in a secure manner. There are, however, two problems with conventional cryptography. First the sender and the receiver should exchange the key through highly reliable and secure channels. The second problem is that a clever eavesdropper can, by a careful analysis of the sent information, reconstruct the key. In this chapter, schemes to overcome these problems are presented. First a scheme for exchanging a key over public channels, the so-called RSA algorithm, is discussed. Then the protocols for the quantum key distribution (QKD), the Bennett–Brassard-84 (BB-84) and Bennett-92(B-92) protocols, are then presented. The QKD protocols are exclusively derived using Bohr’s principle of complementarity. An application of these ideas to the design of secure quantum money is discussed.

KEY PREALLOCATION PROTOCOL BASED ON A SECRET SHARING SCHEME

The symmetric encryption session key generation protocol based on the Shamir secret sharing scheme and the Blom key predistribution scheme is proposed. Predistribution of key materials through secure channels is used. Key calculation is based on symmetric polynomials from three variables. Key calculation is based on threshold scheme (3,4). The basic protocol for two participants was considered. A scheme for an arbitrary number of users has been summarized.

An Improved Diffie-Hellman Protocol Security Using Video Entropy

The Diffie-Hellman is a key exchange protocol to provide a way to transfer shared secret keys between two parties, although those parties might never have communicated together. This paper suggested a new way to transfer keys through public or non-secure channels depending on the sent video files over the channel and then extract keys. The proposed method of key generation depends on the video file content by using the entropy value of the video frames. The proposed system solves the weaknesses in the Diffie-Hellman key exchange algorithm, which is MIMA (Man-in-the-Middle attack) and DLA( Discrete logarithm attack). When the method used high definition videos with a vast amount of data, the keys generated with a large number up to 500 per frame, and each number value reaches more than 1000 to be used or switched when needed. The method also provides some difficulty in guessing the keys from the transmitted video and the reason for the development and emergence of many communication programs Viber, WhatsApp, and other programs, enabling to use the proposed method in these programs.

Block Design Key for Secure Data Sharing in Cloud Computing

Sharing Data in cloud allows multiple participants to share the group of data we are gifting a distinctive block design-based key agreement protocol that supports multiple participants, in whatever the way to make the protection of the data more secure while sharing between two users. Secure data sharing is performed using a private key generated and transmitted using secure channels. A key agreement protocol is used for data transfer to make more protection compare to later communication and this protocol is applied in cloud computing to support secure and economical sharing. In addition, an AES algorithm is used to encrypt the data. We have a susceptibility to estimate Block design key for secure data sharing in cloud, based on key agreement protocol with in which TPA verifies the malicious user from the group. To protect the data more secure every time when user as to download the data the TPA generates key to user. Every time the key is generated as, an OTP to secure the data from the attacker the user must use the same key to decrypt the data. In addition to that, the owner file is also generated to user. TPA acknowledge malignant client from group and detract from group we have a tendency to blessing general recipes for creating the normal meeting key for numerous members.

Privacy-Preserving Distributed Deep Learning via Homomorphic Re-Encryption

The flourishing deep learning on distributed training datasets arouses worry about data privacy. The recent work related to privacy-preserving distributed deep learning is based on the assumption that the server and any learning participant do not collude. Once they collude, the server could decrypt and get data of all learning participants. Moreover, since the private keys of all learning participants are the same, a learning participant must connect to the server via a distinct TLS/SSL secure channel to avoid leaking data to other learning participants. To fix these problems, we propose a privacy-preserving distributed deep learning scheme with the following improvements: (1) no information is leaked to the server even if any learning participant colludes with the server; (2) learning participants do not need different secure channels to communicate with the server; and (3) the deep learning model accuracy is higher. We achieve them by introducing a key transform server and using homomorphic re-encryption in asynchronous stochastic gradient descent applied to deep learning. We show that our scheme adds tolerable communication cost to the deep learning system, but achieves more security properties. The computational cost of learning participants is similar. Overall, our scheme is a more secure and more accurate deep learning scheme for distributed learning participants.